Soc 722

Spring 2025

Stephen Vaisey

Introduction

Objective

This is the first in a two-course sequence designed to help you become competent quantitative researchers in sociology.

This includes learning proper decision making, explanation, computation, visualization, and interpretation.

Schedule

We will normally meet Tuesdays. Please note that we will be meeting on the following Thursdays (not Tuesdays) because of my travel schedule:

  • today
  • January 30
  • February 6
  • February 13
  • March 20

Final exam

The exam will be remote on April 30 from 9am-12pm.

Syllabus

The syllabus is here. Please be sure to read it.

Questions?

Data and Variables

Data structure

country continent year lifeExp pop gdpPercap
Afghanistan Asia 1952 28.801 8425333 779.4453
Afghanistan Asia 1957 30.332 9240934 820.8530
Afghanistan Asia 1962 31.997 10267083 853.1007
Afghanistan Asia 1967 34.020 11537966 836.1971
Afghanistan Asia 1972 36.088 13079460 739.9811
Afghanistan Asia 1977 38.438 14880372 786.1134
Afghanistan Asia 1982 39.854 12881816 978.0114
Afghanistan Asia 1987 40.822 13867957 852.3959
Afghanistan Asia 1992 41.674 16317921 649.3414
Afghanistan Asia 1997 41.763 22227415 635.3414

Tidy format: columns contain variables, each row is an observation.

Untidy data

country continent lifeExp_1952 lifeExp_1957 lifeExp_1962 lifeExp_1967 lifeExp_1972 lifeExp_1977 lifeExp_1982 lifeExp_1987 lifeExp_1992 lifeExp_1997 lifeExp_2002 lifeExp_2007 pop_1952 pop_1957 pop_1962 pop_1967 pop_1972 pop_1977 pop_1982 pop_1987 pop_1992 pop_1997 pop_2002 pop_2007 gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 gdpPercap_1987 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 gdpPercap_2007
Afghanistan Asia 28.801 30.332 31.997 34.020 36.088 38.438 39.854 40.822 41.674 41.763 42.129 43.828 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 25268405 31889923 779.4453 820.8530 853.1007 836.1971 739.9811 786.1134 978.0114 852.3959 649.3414 635.3414 726.7341 974.5803
Albania Europe 55.230 59.280 64.820 66.220 67.690 68.930 70.420 72.000 71.581 72.950 75.651 76.423 1282697 1476505 1728137 1984060 2263554 2509048 2780097 3075321 3326498 3428038 3508512 3600523 1601.0561 1942.2842 2312.8890 2760.1969 3313.4222 3533.0039 3630.8807 3738.9327 2497.4379 3193.0546 4604.2117 5937.0295
Algeria Africa 43.077 45.685 48.303 51.407 54.518 58.014 61.368 65.799 67.744 69.152 70.994 72.301 9279525 10270856 11000948 12760499 14760787 17152804 20033753 23254956 26298373 29072015 31287142 33333216 2449.0082 3013.9760 2550.8169 3246.9918 4182.6638 4910.4168 5745.1602 5681.3585 5023.2166 4797.2951 5288.0404 6223.3675
Angola Africa 30.015 31.999 34.000 35.985 37.928 39.483 39.942 39.906 40.647 40.963 41.003 42.731 4232095 4561361 4826015 5247469 5894858 6162675 7016384 7874230 8735988 9875024 10866106 12420476 3520.6103 3827.9405 4269.2767 5522.7764 5473.2880 3008.6474 2756.9537 2430.2083 2627.8457 2277.1409 2773.2873 4797.2313
Argentina Americas 62.485 64.399 65.142 65.634 67.065 68.481 69.942 70.774 71.868 73.275 74.340 75.320 17876956 19610538 21283783 22934225 24779799 26983828 29341374 31620918 33958947 36203463 38331121 40301927 5911.3151 6856.8562 7133.1660 8052.9530 9443.0385 10079.0267 8997.8974 9139.6714 9308.4187 10967.2820 8797.6407 12779.3796
Australia Oceania 69.120 70.330 70.930 71.100 71.930 73.490 74.740 76.320 77.560 78.830 80.370 81.235 8691212 9712569 10794968 11872264 13177000 14074100 15184200 16257249 17481977 18565243 19546792 20434176 10039.5956 10949.6496 12217.2269 14526.1246 16788.6295 18334.1975 19477.0093 21888.8890 23424.7668 26997.9366 30687.7547 34435.3674
Austria Europe 66.800 67.480 69.540 70.140 70.630 72.170 73.180 74.940 76.040 77.510 78.980 79.829 6927772 6965860 7129864 7376998 7544201 7568430 7574613 7578903 7914969 8069876 8148312 8199783 6137.0765 8842.5980 10750.7211 12834.6024 16661.6256 19749.4223 21597.0836 23687.8261 27042.0187 29095.9207 32417.6077 36126.4927
Bahrain Asia 50.939 53.832 56.923 59.923 63.300 65.593 69.052 70.750 72.601 73.925 74.795 75.635 120447 138655 171863 202182 230800 297410 377967 454612 529491 598561 656397 708573 9867.0848 11635.7995 12753.2751 14804.6727 18268.6584 19340.1020 19211.1473 18524.0241 19035.5792 20292.0168 23403.5593 29796.0483
Bangladesh Asia 37.484 39.348 41.216 43.453 45.252 46.923 50.009 52.819 56.018 59.412 62.013 64.062 46886859 51365468 56839289 62821884 70759295 80428306 93074406 103764241 113704579 123315288 135656790 150448339 684.2442 661.6375 686.3416 721.1861 630.2336 659.8772 676.9819 751.9794 837.8102 972.7700 1136.3904 1391.2538
Belgium Europe 68.000 69.240 70.250 70.940 71.440 72.800 73.930 75.350 76.460 77.530 78.320 79.441 8730405 8989111 9218400 9556500 9709100 9821800 9856303 9870200 10045622 10199787 10311970 10392226 8343.1051 9714.9606 10991.2068 13149.0412 16672.1436 19117.9745 20979.8459 22525.5631 25575.5707 27561.1966 30485.8838 33692.6051
Benin Africa 38.223 40.358 42.618 44.885 47.014 49.190 50.904 52.337 53.919 54.777 54.406 56.728 1738315 1925173 2151895 2427334 2761407 3168267 3641603 4243788 4981671 6066080 7026113 8078314 1062.7522 959.6011 949.4991 1035.8314 1085.7969 1029.1613 1277.8976 1225.8560 1191.2077 1232.9753 1372.8779 1441.2849
Bolivia Americas 40.414 41.890 43.428 45.032 46.714 50.023 53.859 57.251 59.957 62.050 63.883 65.554 2883315 3211738 3593918 4040665 4565872 5079716 5642224 6156369 6893451 7693188 8445134 9119152 2677.3263 2127.6863 2180.9725 2586.8861 2980.3313 3548.0978 3156.5105 2753.6915 2961.6997 3326.1432 3413.2627 3822.1371

Types of variables

Ratio dollars; points (e.g., basketball)
Interval degrees Celsius
Ordinal clothing sizes; Likert scales
Nominal race; sex; country

The first two types are continuous or numeric. The second two types are categorical. Ordinal variables are often treated as numeric and this is usually fine.

Let’s investigate this using the gapminder data. First of all, we’ll keep only the most recent (2007) data.

d <- gapminder |>               
  filter(year == max(year)) |> # keep 2007
  select(-year)                # don't need column

country continent lifeExp pop gdpPercap
Afghanistan Asia 43.828 31889923 974.5803
Albania Europe 76.423 3600523 5937.0295
Algeria Africa 72.301 33333216 6223.3675
Angola Africa 42.731 12420476 4797.2313
Argentina Americas 75.320 40301927 12779.3796
Australia Oceania 81.235 20434176 34435.3674
Austria Europe 79.829 8199783 36126.4927
Bahrain Asia 75.635 708573 29796.0483
Bangladesh Asia 64.062 150448339 1391.2538
Belgium Europe 79.441 10392226 33692.6051

What kinds of variables are these?

The origins of “statistics”

The word statistics comes from the fact that it was information about the state. We’ll focus on information like this for now rather than thinking about samples of individuals.

Visualization basics

Consider two types of plots

  • univariate plots

  • bivariate plots

These are also types of distributions.

Univariate plots

Density plot

ggplot(d,
       aes(x = gdpPercap)) +
  geom_density()

Density plot

Histogram (1)

ggplot(d,
       aes(x = gdpPercap)) +
  geom_histogram(binwidth = 5000,
                 boundary = 0,
                 color = "white")

Histogram (1)

Histogram (2)

ggplot(d,
       aes(x = lifeExp)) +
  geom_histogram(binwidth = 5,
                 boundary = 0,
                 color = "white")

Histogram (2)

Bar graph (univariate)

ggplot(d,
       aes(x = continent)) +
  geom_bar()

Bar graph (univariate)

Bivariate plots

Scatter plot

ggplot(d,
       aes(x = gdpPercap,
           y = lifeExp)) +
         geom_point()

Scatter plot

Bar graph (bivariate)

d |> 
  group_by(continent) |> 
  summarize(GDP = mean(gdpPercap)) |>
  ggplot(aes(x = continent,
             y = GDP)) +
  geom_bar(stat = "identity")

Bar graph (bivariate)

Strip plot

ggplot(d,
       aes(x = gdpPercap,
           y = continent)) +
  geom_point(alpha = .3)

Strip plot

Jittered strip plot

ggplot(d,
       aes(x = gdpPercap,
           y = continent)) +
  geom_jitter(height = .1,
              width = .1,
              alpha = .2)

Jittered strip plot

Time plots (bivariate)

Let’s go back to the full data and look at trends in Oceania.

gapminder |> 
  filter(continent == "Oceania") |> 
  ggplot(aes(x = year,
             y = lifeExp,
             group = country,
             color = country)) +
  geom_line()

Time plots (bivariate)

Your turn!

Homework

  1. Set up a public GitHub repository for this class
  2. Share it with the class on Slack
  3. Create a Quarto document (HW1.qmd) making several visualizations using data of your choice
  4. Render it to html (HW1.html)
  5. Push it to your repository by midnight before the next meeting

Quarto tips

[for later]

Surveys, samples, and probability

Populations and samples

Studying populations is nice:

  • all countries in the world
  • all states in the US
  • all cities in a state

However, we cannot study (say) all adults in a country. So we usually work with samples. This raises the issue of using samples to make inferences about populations.

Simple random sampling

Sampling where every eligible case has an equal probability of selection.

Note

In real-life surveys, simple random sampling is pretty uncommon. But it’s important as a baseline!

Simulations

We can use simulations to build intuition about sampling.

A simulation is when we make up “true data”, hide it from ourselves, and see how well we can figure out the the truth using some procedure.

Simple survey

Imagine a city of 100,000 adults. Of these, 70,000 (i.e., 70%) have at least one child.

How close could we get to this number by drawing different random samples?

A first simulation

Let’s set up the “true” population:

population <- tibble(id = 1:1e5) |> # initialize with 100K rows
  mutate(parent = if_else(id <= 70000, "Yes", "No"))  # first 70K "yes"

This simple code makes the first 70,000 rows “yes” and the next 30,000 “no.” We now know the “truth”, which we can use for comparison.

Visualizing the population

Data types

Because of the way we created it, parent will be a character <chr> variable. We often use 1 to mean “yes” and 0 to mean “no” in statistics. We could add a numeric version of parent as follows.

population <- population |> 
  mutate(parent_num = if_else(parent == "Yes", 1L, 0L))

Tip

Assigning an object to its “old” name allows you to add things to the original object. In this case, we are adding a new column, parent_num.

Checking data type

We can use glimpse() to easily see what type of variable things are.

glimpse(population)
Rows: 100,000
Columns: 3
$ id         <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15…
$ parent     <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", …
$ parent_num <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…

Tip

glimpse() is very useful. It shows you your data “sideways” so you can see information about all the columns (which are shown as rows).

Implications of data type

Data type affects what we can do to a variable (or column). For example, we can take the mean (average) of a set of numbers but we can’t take the mean of a set of characters.

population |>
  summarize(mean1 = mean(parent),
            mean2 = mean(parent_num))
# A tibble: 1 × 2
  mean1 mean2
  <dbl> <dbl>
1    NA   0.7

Note

In R, NA stands for “not available” and means that the data are missing. This cell is missing because what we asked for couldn’t be calculated.

Aside: doing stuff to a variable

We can pick out a column to operate on in two ways:

tidyverse

population |> 
  pull(parent_num) |> 
  mean()
[1] 0.7

base R

mean(population$parent_num)
[1] 0.7

Now back to our story…

Parameters and statistics

In our city, 70% is the population parameter because exactly 70,000 out of 100,000 people actually have at least one child.

We can’t afford to ask everyone, though. So what if we asked, say, 1000 randomly selected adults. Then we could compute the proportion of the sample that has a child. This would be a sample statistic. We use sample statistics to make inferences about population parameters.

Drawing a sample

set.seed(722)
my_sample <- population |> 
  slice_sample(n = 1000,
               replace = FALSE)

Sampling is a random process. We will get a different result every time. By using set.seed(), we ensure we get the same “random” result every time the code is run.

Note

Sampling theory is based on sampling with replacement. However, to make it more straightforward, we will use sampling without replacement here.

The sample statistic

To estimate the population proportion, we will use the sample proportion.

my_sample |> 
  group_by(parent) |> 
  summarize(n = n())
# A tibble: 2 × 2
  parent     n
  <chr>  <int>
1 No       318
2 Yes      682

In our sample, 682 people are parents. This is 68.2%, which isn’t exactly the 70% in the population. This is because we randomly sampled from our population. It could be higher or lower.

Repeating the experiment

In real life, we only get to sample once. Sampling is expensive! But since this is just a simulation, we can ask what would happen if we sampled 1000 people many, many times.

A custom function

We can first make a function that does what we want once. This is hard at first but usually pays off.

get_count <- function(n = 1000) {       # default n = 1000
  slice_sample(population, n = n) |>    # take a sample
    summarize(sum = sum(parent_num)) |> # count the parents
    as.integer()                        # save the number
}

Tip

If you run all the code up to here, you can call get_count() interactively in the console many times to get a feel for it.

Iterating

This would seem to make sense, but it doesn’t work.

set.seed(722)
my_bad_samples <- tibble(
  sample_id = 1:100,
  samp_count = get_count(n = 1000))
head(my_bad_samples)
# A tibble: 6 × 2
  sample_id samp_count
      <int>      <int>
1         1        682
2         2        682
3         3        682
4         4        682
5         5        682
6         6        682

Iterating with rowwise()

set.seed(722)
my_samples <- tibble(
  sample_id = 1:100) |> 
  rowwise() |> 
  mutate(samp_count = get_count(n = 1000))
head(my_samples, n = 3)
# A tibble: 3 × 2
# Rowwise: 
  sample_id samp_count
      <int>      <int>
1         1        682
2         2        716
3         3        694

Tip

I don’t need n = 1000 because I set it as the default when I made my function.

Plotting the results

ggplot(my_samples,
       aes(x = samp_count)) +
  geom_histogram(aes(y = ..density..), # for overlay
                 boundary = 697.5,     # why would I choose this?
                 binwidth = 5,         # somewhat arbitrary
                 color = "white",
                 fill = "gray") +
  geom_density(color = "#36454f",      # overlay density
               linewidth = 1,
               alpha = .5)

Plotting the results

Repeating with 2,500 samples

Sample size and number of samples

When we are doing simulations like this, it can be easy to confuse the sample size (1000) with the number of samples in our simulation (2500). They are not the same thing!

The sample size is the number of people we would survey “in the real world”.

The number of samples is how many times we want to run our simulated experiment.

How accurate are we?

We will do this formally later. But now we can quantify how accurately a sample proportion of 1000 people might estimate this population proportion by using the interquartile range. This is how wide the middle half of the data is.

my_many_samples |> pull(samp_count) |> quantile(c(.25, .75))
25% 75% 
691 710 
my_many_samples |> pull(samp_count) |> IQR()
[1] 19

Reminder

Remember: in real life we only get one of these samples.

Visualizing IQR

Sample size

Remember that we drew a sample of 1000 people to estimate our sample proportions. What if we had different sample sizes? Let’s compare the following:

  • N = 60
  • N = 250
  • N = 1000

Visualizing “accuracy”

Proportions

The x-axis is now proportion because we can no longer compare raw counts.

Comparing IQR

The interquartile ranges (i.e., widths of the middle half of the data) decrease a lot with sample size.

# A tibble: 3 × 2
  sample_size    IQR
  <chr>        <dbl>
1 N = 1000    0.0190
2 N = 250     0.0360
3 N = 60      0.0667

Warning

We will explore these issues more formally very soon using the concepts sampling distribution and standard error. For now, the goal is to understand how to use simulations to build qualitative intuition about sample size.

Thinking with real data: GSS

The General Social Survey is a repeated cross-sectional survey that has been fielded every year or other year since 1972. It is the “Hubble Telescope” of sociology!

Accessing the GSS in R

Install Kieran Healy’s gssr package using the code below. You only have to do this once.

# Install 'gssr' from 'ropensci' universe
install.packages('gssr', repos =
  c('https://kjhealy.r-universe.dev', 'https://cloud.r-project.org'))

# Also recommended: install 'gssrdoc' as well
install.packages('gssrdoc', repos =
  c('https://kjhealy.r-universe.dev', 'https://cloud.r-project.org'))

Getting the 2022 survey

library(gssr)

gss2022 <- gss_get_yr(year = 2022) |> # get 2022
  haven::zap_labels()                 # remove Stata value labels

glimpse(gss2022)
Rows: 4,149
Columns: 1,185
$ year           <dbl> 2022, 2022, 2022, 2022, 2022, 2022, 2022, 202…
$ id             <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14…
$ wrkstat        <dbl> 1, 5, 1, 3, 8, 1, 2, 1, 1, 5, 7, 2, 1, 2, 7, …
$ hrs1           <dbl> 40, NA, 52, NA, NA, 50, 30, 40, 31, NA, NA, 3…
$ hrs2           <dbl> NA, NA, NA, 25, NA, NA, NA, NA, NA, NA, NA, N…
$ evwork         <dbl> NA, 1, NA, NA, 1, NA, NA, NA, NA, 1, 1, NA, N…
$ wrkslf         <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ occ10          <dbl> 430, 50, 4610, 4120, 7330, 4610, 1105, 2200, …
$ prestg10       <dbl> 39, 53, 48, 34, 38, 48, 61, 74, 31, 48, 39, 3…
$ prestg105plus  <dbl> 42, 73, 44, 29, 37, 44, 82, 94, 16, 50, 46, 2…
$ indus10        <dbl> 6290, 6695, 8290, 8660, 3390, 8180, 8090, 787…
$ marital        <dbl> 3, 1, 3, 5, 5, 5, 5, 1, 5, 5, 2, 5, 1, 5, 5, …
$ martype        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ divorce        <dbl> NA, 2, NA, NA, NA, NA, NA, 2, NA, NA, 2, NA, …
$ widowed        <dbl> 2, 1, 2, NA, NA, NA, NA, 2, NA, NA, NA, NA, 2…
$ spwrksta       <dbl> NA, 5, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA,…
$ sphrs1         <dbl> NA, NA, NA, NA, NA, NA, NA, 40, NA, NA, NA, N…
$ sphrs2         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ spevwork       <dbl> NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ cowrksta       <dbl> NA, NA, NA, NA, 7, 1, NA, NA, NA, NA, NA, NA,…
$ coevwork       <dbl> NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, NA…
$ cohrs1         <dbl> NA, NA, NA, NA, NA, 37, NA, NA, NA, NA, NA, N…
$ cohrs2         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ spwrkslf       <dbl> NA, 2, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA,…
$ sppres80       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ spocc10        <dbl> NA, 2340, NA, NA, NA, NA, NA, 2200, NA, NA, N…
$ sppres10       <dbl> NA, 38, NA, NA, NA, NA, NA, 74, NA, NA, NA, N…
$ sppres105plus  <dbl> NA, 32, NA, NA, NA, NA, NA, 94, NA, NA, NA, N…
$ spind10        <dbl> NA, 6695, NA, NA, NA, NA, NA, 7870, NA, NA, N…
$ coocc10        <dbl> NA, NA, NA, NA, 3600, 4700, NA, NA, NA, NA, N…
$ coind10        <dbl> NA, NA, NA, NA, 8270, 5790, NA, NA, NA, NA, N…
$ pawrkslf       <dbl> 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, NA, 1, 2, 2, NA…
$ paocc10        <dbl> 800, 8900, 4220, 8965, 6260, 4850, 9620, 9130…
$ papres10       <dbl> 60, 35, 24, 35, 28, 45, 25, 35, 39, 25, NA, 3…
$ papres105plus  <dbl> 85, 29, 15, 28, 23, 55, 16, 29, 40, 16, NA, 4…
$ paind10        <dbl> 7280, 1370, 7860, 2990, 770, 1870, 4470, 4470…
$ mawrkslf       <dbl> 2, NA, 2, 2, 2, 2, NA, 2, 1, NA, 2, 1, 2, 2, …
$ maocc10        <dbl> 2010, NA, 4720, 3255, 5700, 5240, NA, 5140, 4…
$ mapres10       <dbl> 54, NA, 28, 64, 47, 31, NA, 40, 31, NA, 48, 3…
$ mapres105plus  <dbl> 68, NA, 16, 87, 55, 20, NA, 32, 16, NA, 44, 4…
$ maind10        <dbl> 9480, NA, 4970, 8190, 3490, 4970, NA, 580, 86…
$ sibs           <dbl> 1, 3, 1, 1, 2, 1, 3, 3, 4, 6, 15, 2, 4, 4, 4,…
$ childs         <dbl> 1, 2, 1, 0, 2, 0, 0, 0, 1, 4, 2, 0, 2, 2, 3, …
$ age            <dbl> 72, 80, 57, 23, 62, 27, 20, 47, 31, 72, 57, 2…
$ agekdbrn       <dbl> 27, 24, 27, NA, 21, NA, NA, NA, 28, 21, 21, N…
$ educ           <dbl> 16, 18, 12, 16, 14, 12, 12, 16, 12, 12, 13, 1…
$ paeduc         <dbl> 16, 12, 14, 14, 12, 12, 11, 6, 12, NA, NA, 8,…
$ maeduc         <dbl> 16, 12, 11, 18, 16, 14, 9, 18, 10, NA, NA, 18…
$ speduc         <dbl> NA, 16, NA, NA, NA, NA, NA, 16, NA, NA, NA, N…
$ coeduc         <dbl> NA, NA, NA, NA, 12, 12, NA, NA, NA, NA, NA, N…
$ codeg          <dbl> NA, NA, NA, NA, 1, 1, NA, NA, NA, NA, NA, NA,…
$ degree         <dbl> 3, 4, 1, 3, 1, 1, 1, 4, 1, 1, 1, 3, 1, 1, 1, …
$ padeg          <dbl> 3, 1, 1, 1, 1, 1, 1, 0, 1, NA, NA, 0, 1, 1, N…
$ madeg          <dbl> 4, 1, 0, 4, 3, 2, 0, 4, 0, NA, NA, 4, 1, 1, 1…
$ spdeg          <dbl> NA, 3, NA, NA, NA, NA, NA, 4, NA, NA, NA, NA,…
$ major1         <dbl> 44, 9, NA, 44, NA, NA, NA, 16, NA, NA, NA, 25…
$ major2         <dbl> NA, NA, NA, 70, NA, NA, NA, NA, NA, NA, NA, 2…
$ dipged         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, …
$ sex            <dbl> 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, …
$ race           <dbl> 1, 1, 1, 1, 1, 1, 3, 1, 1, NA, 1, 1, 1, 1, 1,…
$ res16          <dbl> 5, 5, 3, 3, 3, 3, 6, 4, 3, 3, 4, 4, 3, 4, 5, …
$ reg16          <dbl> 2, 3, 1, 1, 1, 2, 2, 9, 5, 2, 2, 2, 2, 1, 1, …
$ mobile16       <dbl> 3, 3, 2, 1, 1, 1, 1, 3, 3, 1, 1, 2, 2, 1, 2, …
$ family16       <dbl> 1, 1, 3, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 5, …
$ famdif16       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mawrkgrw       <dbl> 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, NA,…
$ incom16        <dbl> 4, 2, 2, 4, 3, 1, 3, 2, 3, 2, 5, 4, 2, 2, 3, …
$ born           <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ parborn        <dbl> 0, 0, 0, 0, 0, 0, 8, 1, 0, 0, 0, 0, 1, 0, 3, …
$ granborn       <dbl> 4, NA, 1, 0, 2, 0, 4, 4, 0, NA, 0, 0, 2, 0, N…
$ hompop         <dbl> 1, 2, NA, 3, NA, 2, NA, NA, NA, NA, NA, NA, 6…
$ babies         <dbl> 0, 0, NA, 0, NA, 0, NA, NA, NA, NA, NA, NA, 0…
$ preteen        <dbl> 0, 0, NA, 0, NA, 0, NA, NA, NA, NA, NA, NA, 2…
$ teens          <dbl> 0, 0, NA, 0, NA, 0, NA, NA, NA, NA, NA, NA, 0…
$ adults         <dbl> 1, 2, 3, 3, 3, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, …
$ unrelat        <dbl> NA, 0, 1, 0, 0, 1, NA, 0, 0, NA, 0, NA, 0, 0,…
$ earnrs         <dbl> 1, 0, 1, 3, 1, 2, 1, 2, 1, 0, 1, 1, 4, 2, 2, …
$ income         <dbl> 12, NA, 12, 12, 12, 12, 12, 12, 11, 9, 12, 12…
$ rincome        <dbl> 12, NA, 12, 5, NA, 12, 12, 12, 10, NA, NA, 12…
$ income16       <dbl> 22, NA, 18, 22, 15, 19, 17, 26, 14, 9, 19, 16…
$ rincom16       <dbl> 22, NA, 18, 5, NA, 19, 17, 21, 11, NA, NA, 16…
$ region         <dbl> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, …
$ xnorcsiz       <dbl> 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
$ srcbelt        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ size           <dbl> 10, 10, 10, 10, 10, 24, 24, 24, 24, 24, 24, 2…
$ partyid        <dbl> 0, 3, 5, 0, 3, 1, 2, 0, 2, 0, 3, 2, 7, 5, 3, …
$ vote16         <dbl> 1, 1, 1, 3, 1, 1, 3, 1, 1, 1, 1, 2, 2, NA, 1,…
$ pres16         <dbl> 1, 1, 2, NA, 2, 1, NA, 1, 3, 3, 1, NA, NA, NA…
$ if16who        <dbl> NA, NA, NA, 1, NA, NA, 2, NA, NA, NA, NA, 4, …
$ polviews       <dbl> 2, 5, 4, 1, 5, 4, 3, 1, 3, 4, 4, NA, 4, 4, 6,…
$ natspac        <dbl> 2, 1, NA, 3, NA, 2, NA, NA, NA, NA, NA, NA, 2…
$ natenvir       <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, 1…
$ natheal        <dbl> 1, 2, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, 1…
$ natcity        <dbl> 2, 1, NA, 1, NA, 2, NA, NA, NA, NA, NA, NA, 1…
$ natcrime       <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, 1…
$ natdrug        <dbl> 2, 2, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, 1…
$ nateduc        <dbl> 1, 2, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, 1…
$ natrace        <dbl> 1, 3, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, 1…
$ natarms        <dbl> 2, 2, NA, 3, NA, 3, NA, NA, NA, NA, NA, NA, 3…
$ nataid         <dbl> 2, 2, NA, 2, NA, 2, NA, NA, NA, NA, NA, NA, 3…
$ natfare        <dbl> 2, 3, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, 1…
$ natroad        <dbl> 2, 1, 1, 2, 2, 1, 3, 1, NA, 1, 2, 1, 2, 2, 3,…
$ natsoc         <dbl> 1, 1, 2, 1, 1, 2, NA, 1, NA, 1, 1, NA, 1, 1, …
$ natmass        <dbl> 2, 2, 1, 1, 2, 1, 2, 1, NA, NA, 2, 1, 1, 2, 3…
$ natpark        <dbl> 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, …
$ natchld        <dbl> 3, 2, 2, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ natsci         <dbl> 1, 2, 1, 3, 3, 2, 1, 2, 2, NA, 2, NA, 2, 1, 1…
$ natenrgy       <dbl> 2, 2, 3, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, …
$ natspacy       <dbl> NA, NA, 1, NA, 3, NA, 3, 3, NA, 3, 3, NA, NA,…
$ natenviy       <dbl> NA, NA, 1, NA, 1, NA, 1, 1, 1, NA, 1, 1, NA, …
$ nathealy       <dbl> NA, NA, 1, NA, 1, NA, 1, 1, 1, 1, 1, 1, NA, N…
$ natcityy       <dbl> NA, NA, 3, NA, 3, NA, 3, 1, NA, 1, 3, 2, NA, …
$ natcrimy       <dbl> NA, NA, 1, NA, 1, NA, 3, 3, NA, 1, 2, 3, NA, …
$ natdrugy       <dbl> NA, NA, 1, NA, 3, NA, 1, 1, 1, 1, 1, 1, NA, N…
$ nateducy       <dbl> NA, NA, 1, NA, 2, NA, 1, 1, 1, 2, 1, 1, NA, N…
$ natracey       <dbl> NA, NA, 2, NA, 3, NA, 1, 1, NA, 1, 1, 1, NA, …
$ natarmsy       <dbl> NA, NA, 1, NA, 2, NA, 3, 3, 3, 1, 3, 3, NA, N…
$ nataidy        <dbl> NA, NA, 3, NA, 3, NA, 1, 2, 3, 3, 2, 3, NA, N…
$ natfarey       <dbl> NA, NA, 1, NA, 1, NA, 1, 1, 1, 1, 1, 1, NA, N…
$ eqwlth         <dbl> 1, NA, 5, NA, 1, 1, 3, 1, NA, 7, 4, 1, 2, 4, …
$ tax            <dbl> 1, 1, 1, 1, 1, 1, NA, 2, 1, NA, NA, 2, NA, 3,…
$ spkath         <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ colath         <dbl> 4, 4, 5, 4, 4, 4, 4, 4, 5, NA, NA, 4, NA, 5, …
$ libath         <dbl> 2, 2, NA, 2, NA, 2, NA, NA, NA, NA, NA, NA, N…
$ spkrac         <dbl> 1, NA, NA, 2, NA, 1, NA, NA, NA, NA, NA, NA, …
$ colrac         <dbl> 5, 4, 5, 5, 4, 5, 5, 5, 5, NA, NA, 5, NA, 5, …
$ librac         <dbl> 2, 2, NA, 2, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ spkcom         <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ colcom         <dbl> 5, 5, NA, 5, NA, 5, NA, NA, NA, NA, NA, NA, N…
$ libcom         <dbl> 2, 2, NA, 2, NA, 2, NA, NA, NA, NA, NA, NA, N…
$ spkmslm        <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ colmslm        <dbl> 5, 5, 5, 4, 4, 5, 5, 4, 5, NA, NA, 5, NA, 5, …
$ libmslm        <dbl> 2, 2, NA, 2, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ cappun         <dbl> 2, 1, 1, 2, 2, 2, 2, 2, 1, NA, 2, 2, 1, 1, 1,…
$ gunlaw         <dbl> 1, 2, 1, 1, 2, 1, 1, 1, NA, NA, NA, NA, NA, 1…
$ courts         <dbl> NA, NA, NA, NA, NA, NA, 3, NA, 2, 2, NA, NA, …
$ grass          <dbl> NA, NA, NA, NA, NA, NA, 2, NA, NA, 1, NA, 1, …
$ relig          <dbl> 4, 2, 5, 4, 2, 4, 4, 4, 4, 5, 4, 4, 10, 2, 2,…
$ denom          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ other          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ jew            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ fund           <dbl> 3, 2, NA, 3, 2, 3, 3, 3, 3, NA, 3, 3, 3, 2, 2…
$ attend         <dbl> 2, 2, 0, 4, 2, 0, 1, 1, 0, 7, 0, 1, 2, 1, 4, …
$ reliten        <dbl> NA, NA, NA, NA, NA, NA, 4, NA, 4, 1, NA, 4, 2…
$ postlife       <dbl> NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, 2,…
$ pray           <dbl> 6, 5, 5, 4, 4, 6, 4, 6, 3, 1, 2, 5, 5, 5, 1, …
$ popespks       <dbl> NA, 4, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA,…
$ relig16        <dbl> 4, 2, 1, 2, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, …
$ denom16        <dbl> NA, NA, 70, NA, NA, NA, 60, NA, NA, 18, 28, N…
$ oth16          <dbl> NA, NA, NA, NA, NA, NA, 77, NA, NA, NA, NA, N…
$ jew16          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ fund16         <dbl> 3, 2, 2, 2, 2, 2, 1, 2, 2, 1, 3, 2, 2, 2, 2, …
$ sprel          <dbl> NA, 2, NA, NA, NA, NA, NA, 4, NA, NA, NA, NA,…
$ spden          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ spother        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ spjew          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ spfund         <dbl> NA, 2, NA, NA, NA, NA, NA, 3, NA, NA, NA, NA,…
$ corel          <dbl> NA, NA, NA, NA, 2, 4, NA, NA, NA, NA, NA, NA,…
$ coden          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ coother        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ cojew          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ cofund         <dbl> NA, NA, NA, NA, 2, 3, NA, NA, NA, NA, NA, NA,…
$ prayer         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, 2, NA, NA,…
$ bible          <dbl> NA, NA, NA, NA, NA, NA, 3, NA, 3, 1, NA, 3, 2…
$ racopen        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ raclive        <dbl> 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1,…
$ affrmact       <dbl> NA, 4, NA, 3, NA, NA, NA, NA, 4, 3, 2, NA, 2,…
$ wrkwayup       <dbl> NA, 1, NA, 4, NA, NA, NA, NA, 4, 2, 3, NA, 5,…
$ happy          <dbl> 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 3, 2, …
$ hapmar         <dbl> NA, 2, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA,…
$ hapcohab       <dbl> NA, NA, NA, NA, 2, 1, NA, NA, NA, NA, NA, NA,…
$ health         <dbl> 2, 2, 2, 2, 3, 1, 2, 2, 2, 1, 2, 3, 3, 2, 1, …
$ life           <dbl> 2, 2, 2, 2, 2, 1, 2, 2, 1, NA, NA, 2, NA, 2, …
$ helpful        <dbl> NA, NA, NA, NA, NA, NA, 3, NA, NA, 1, NA, 2, …
$ fair           <dbl> NA, NA, NA, NA, NA, NA, 1, NA, NA, 2, NA, 1, …
$ trust          <dbl> NA, NA, NA, NA, NA, NA, 2, NA, NA, 2, NA, 2, …
$ confinan       <dbl> 2, NA, 3, NA, 2, 3, 2, 3, NA, 1, 3, 3, 2, 2, …
$ conbus         <dbl> 3, NA, 2, NA, 2, 3, 2, 3, NA, 2, 2, 3, 2, 2, …
$ conclerg       <dbl> 3, NA, 1, NA, 2, 3, NA, 2, NA, 1, 3, 2, 2, 2,…
$ coneduc        <dbl> 3, NA, 3, NA, 2, 1, 2, 2, NA, 1, 1, 2, 2, 2, …
$ confed         <dbl> 1, NA, 3, NA, 3, 3, 2, 2, NA, 2, 3, 3, 2, 2, …
$ conlabor       <dbl> 2, NA, 2, NA, 2, 3, 3, 1, NA, 2, 2, 2, 3, 2, …
$ conpress       <dbl> 2, NA, 3, NA, 3, 3, 3, 1, NA, 2, 3, 2, 3, 2, …
$ conmedic       <dbl> 1, NA, 2, NA, 2, 3, 2, 1, NA, 2, 1, 2, 2, 2, …
$ contv          <dbl> 2, NA, 3, NA, 2, 3, 2, 2, NA, 1, 2, 2, 3, 2, …
$ conjudge       <dbl> 3, NA, 1, NA, 2, 3, NA, 2, NA, 2, 3, 3, 3, 2,…
$ consci         <dbl> 1, NA, 2, NA, 1, 3, 1, 1, NA, NA, 1, 2, 2, 2,…
$ conlegis       <dbl> 3, NA, 3, NA, 3, 3, 2, 2, NA, 3, 3, 3, 3, 2, …
$ conarmy        <dbl> 2, NA, 2, NA, 1, 3, 2, 2, NA, 1, 1, 3, 2, 1, …
$ obey           <dbl> 5, NA, 4, NA, 4, 4, 5, 5, NA, 4, 5, NA, NA, 3…
$ popular        <dbl> 4, NA, 5, NA, 5, 5, 4, 4, NA, 5, 4, NA, NA, 5…
$ thnkself       <dbl> 1, NA, 1, NA, 2, 1, 1, 1, NA, 2, 1, NA, NA, 4…
$ workhard       <dbl> 3, NA, 2, NA, 1, 2, 2, 3, NA, 1, 3, NA, NA, 2…
$ helpoth        <dbl> 2, NA, 3, NA, 3, 3, 3, 2, NA, 3, 2, NA, NA, 1…
$ socrel         <dbl> NA, 5, NA, 3, NA, NA, NA, NA, 2, 5, 2, NA, 2,…
$ socommun       <dbl> NA, 6, NA, 3, NA, NA, NA, NA, 3, 7, 4, NA, 5,…
$ socfrend       <dbl> NA, 5, NA, 3, NA, NA, NA, NA, 7, 7, 2, NA, 5,…
$ socbar         <dbl> NA, 5, NA, 3, NA, NA, NA, NA, 6, 7, 5, NA, 5,…
$ aged           <dbl> NA, NA, NA, NA, NA, NA, 3, NA, NA, 2, NA, 1, …
$ weekswrk       <dbl> 52, 0, 47, 40, 0, 50, 52, 52, 32, 0, 0, 51, 5…
$ partfull       <dbl> 1, NA, 1, 1, NA, 1, 1, 1, 2, NA, NA, 2, 1, 2,…
$ joblose        <dbl> NA, NA, NA, 4, NA, NA, NA, NA, 4, NA, NA, NA,…
$ jobfind        <dbl> NA, NA, NA, 1, NA, NA, NA, NA, 2, NA, NA, NA,…
$ satjob         <dbl> 3, NA, 3, 3, NA, 2, 3, 2, 1, NA, 2, 1, 1, 3, …
$ richwork       <dbl> 2, NA, 1, 1, NA, 1, 1, 2, 1, NA, NA, 1, NA, 1…
$ class          <dbl> 3, 3, 2, 3, 3, 1, 1, 4, 2, 2, 2, 3, 2, 3, 2, …
$ rank           <dbl> NA, 4, NA, 4, NA, NA, NA, NA, 5, NA, NA, NA, …
$ satfin         <dbl> 3, 2, 2, 1, 3, 2, 3, 1, 3, 1, 2, 2, 2, 2, 3, …
$ finalter       <dbl> 3, 2, 2, 3, 2, 3, 1, 2, 3, 3, 2, 1, 3, 2, 2, …
$ finrela        <dbl> 3, 3, 3, 4, 2, 3, 2, 5, 3, 3, 2, 4, 3, 2, 1, …
$ wksub          <dbl> 1, NA, 1, 1, NA, 1, 1, 1, 1, NA, NA, 1, 1, 2,…
$ wksubs         <dbl> 3, NA, 3, 3, NA, 3, 3, 3, 4, NA, NA, 4, 3, NA…
$ wksub1         <dbl> 1, NA, 1, 1, NA, 1, 1, 1, 1, NA, NA, 1, 1, 2,…
$ wksubs1        <dbl> 3, NA, 3, 3, NA, 3, 3, 3, 4, NA, NA, 4, 3, NA…
$ wksup          <dbl> 2, NA, 2, 2, NA, 2, 2, 2, 1, NA, NA, 2, 1, 2,…
$ wksups         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, NA, NA…
$ wksup1         <dbl> 2, NA, 2, 2, NA, 2, 2, 2, 1, NA, NA, 2, 1, 2,…
$ wksups1        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, NA, NA…
$ unemp          <dbl> 2, NA, 2, NA, 2, 2, 2, 2, NA, 2, 2, 1, 1, 2, …
$ union          <dbl> 4, NA, 1, NA, 4, 4, NA, 4, NA, 1, 4, 4, 4, 4,…
$ union1         <dbl> 4, NA, 1, NA, 4, 4, NA, 4, NA, 1, 4, 4, 4, 4,…
$ getahead       <dbl> NA, NA, NA, NA, NA, NA, 2, NA, 2, NA, NA, 2, …
$ parsol         <dbl> 4, NA, 3, NA, 4, 5, 1, 1, NA, 1, 1, 2, 4, 3, …
$ kidssol        <dbl> NA, NA, NA, NA, NA, NA, 1, NA, NA, 1, NA, NA,…
$ fepol          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 2, 2, NA, NA,…
$ abdefect       <dbl> NA, NA, 1, NA, 1, NA, 1, 1, 1, NA, NA, 1, NA,…
$ abnomore       <dbl> NA, NA, 1, NA, 2, NA, 1, 1, 1, NA, NA, 1, NA,…
$ abhlth         <dbl> NA, NA, 1, NA, 1, NA, 1, 1, 1, NA, NA, 1, NA,…
$ abpoor         <dbl> NA, NA, 1, NA, 1, NA, 1, 1, 1, NA, NA, 1, NA,…
$ abrape         <dbl> NA, NA, 1, NA, 1, NA, 1, 1, 1, NA, NA, 1, NA,…
$ absingle       <dbl> NA, NA, 1, NA, 2, NA, 1, 1, 1, NA, NA, 1, NA,…
$ abany          <dbl> NA, NA, 1, NA, 1, NA, 1, 1, 1, NA, NA, 1, NA,…
$ chldidel       <dbl> NA, 3, NA, 8, NA, NA, NA, NA, 8, 8, 8, NA, 2,…
$ pillok         <dbl> NA, 2, NA, 2, NA, NA, NA, NA, 3, 2, 1, NA, 1,…
$ sexeduc        <dbl> NA, 1, NA, 1, NA, NA, NA, NA, 1, 2, 1, NA, 1,…
$ divlaw         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA…
$ premarsx       <dbl> NA, 4, NA, 4, NA, NA, NA, NA, 4, 4, 4, NA, 4,…
$ teensex        <dbl> NA, 1, NA, 3, NA, NA, NA, NA, 1, 4, 3, NA, 3,…
$ xmarsex        <dbl> 1, 2, 2, 3, 1, 3, 3, 1, 2, NA, NA, 3, NA, 1, …
$ homosex        <dbl> 4, 3, 4, 4, 1, 4, 4, 4, 4, NA, NA, 4, NA, 4, …
$ pornlaw        <dbl> 2, NA, 2, NA, 1, 2, 2, 2, NA, 2, 2, 2, 2, 2, …
$ xmovie         <dbl> 2, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA,…
$ spanking       <dbl> NA, 3, NA, 2, NA, NA, NA, NA, 3, 2, 3, NA, 3,…
$ letdie1        <dbl> NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA,…
$ suicide1       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, NA, …
$ suicide2       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 2, 2, 2, NA, …
$ suicide3       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 2, 2, 2, NA, …
$ suicide4       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 2, 2, 2, NA, …
$ polhitok       <dbl> 1, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA,…
$ polabuse       <dbl> 2, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, NA,…
$ polmurdr       <dbl> 2, NA, 2, NA, 2, 2, 2, 2, NA, 1, 2, 2, 2, 2, …
$ polescap       <dbl> 1, NA, 1, NA, 1, 1, 1, 2, NA, 2, 1, NA, 2, 2,…
$ polattak       <dbl> 1, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA,…
$ fear           <dbl> 2, 1, 2, 2, 2, 1, 1, 2, 1, NA, NA, 1, NA, 2, …
$ owngun         <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, NA, 2, …
$ pistol         <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, NA, 2, …
$ shotgun        <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, NA, 2, …
$ rifle          <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, NA, 2, …
$ rowngun        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ hunt           <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, NA, NA, 4, NA, 4, …
$ hunt1          <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, NA, NA, 4, NA, 4, …
$ news           <dbl> NA, 1, NA, 5, NA, NA, NA, NA, 4, 5, 4, NA, 2,…
$ tvhours        <dbl> NA, 3, NA, 1, NA, NA, NA, NA, 0, 19, 2, NA, 2…
$ phone          <dbl> 1, 1, 6, 6, 6, 6, 6, 2, 6, 6, 1, 2, 6, 6, 1, …
$ coop           <dbl> 1, NA, NA, NA, NA, NA, 1, NA, 1, 1, NA, 1, 1,…
$ comprend       <dbl> 1, NA, NA, NA, NA, NA, 1, NA, 1, 1, NA, 2, 1,…
$ form           <dbl> 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, …
$ fechld         <dbl> NA, 3, NA, 2, NA, NA, NA, NA, 2, 1, 2, NA, 1,…
$ fepresch       <dbl> NA, 2, NA, 3, NA, NA, NA, NA, 3, 3, 3, NA, 4,…
$ fefam          <dbl> NA, 4, NA, 4, NA, NA, NA, NA, 4, 3, 4, NA, 4,…
$ racdif1        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, NA,…
$ racdif2        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, 2, NA,…
$ racdif3        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, 2, 1, NA, …
$ racdif4        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 2, NA,…
$ helppoor       <dbl> 2, NA, 4, NA, 1, 1, 1, 1, NA, 3, 3, 1, 2, 3, …
$ helpnot        <dbl> 2, NA, 5, NA, 1, 1, 2, 1, NA, 3, 3, 1, 4, 3, …
$ helpsick       <dbl> 1, NA, 4, NA, 1, 1, 2, 1, NA, 2, 1, 1, 3, 3, …
$ helpblk        <dbl> 2, NA, 4, NA, 5, 1, NA, 1, NA, 2, 3, 1, 2, 3,…
$ god            <dbl> 1, NA, 6, NA, 5, 1, 3, 2, NA, 6, 3, 2, 5, 3, …
$ reborn         <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, …
$ savesoul       <dbl> 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ wlthwhts       <dbl> NA, 4, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA,…
$ wlthblks       <dbl> NA, 5, NA, 6, NA, NA, NA, NA, NA, NA, NA, NA,…
$ wlthhsps       <dbl> NA, 5, NA, 4, NA, NA, NA, NA, NA, NA, NA, NA,…
$ workwhts       <dbl> NA, 3, NA, 4, NA, NA, NA, NA, 4, 4, 4, NA, 4,…
$ workblks       <dbl> NA, 4, NA, 3, NA, NA, NA, NA, 4, 4, 4, NA, 4,…
$ workhsps       <dbl> NA, 3, NA, 2, NA, NA, NA, NA, 4, 4, 4, NA, 4,…
$ intlwhts       <dbl> NA, 5, NA, 4, NA, NA, NA, NA, 4, 4, 4, NA, 4,…
$ intlblks       <dbl> NA, 3, NA, 4, NA, NA, NA, NA, 4, 4, 4, NA, 4,…
$ intlhsps       <dbl> NA, 3, NA, 4, NA, NA, NA, NA, 4, 4, 4, NA, 4,…
$ liveblks       <dbl> NA, 5, NA, 1, NA, NA, NA, NA, 3, 3, 1, NA, 3,…
$ marblk         <dbl> NA, 4, NA, 3, NA, NA, NA, NA, 3, 3, 1, NA, 3,…
$ marasian       <dbl> NA, 3, NA, 3, NA, NA, NA, NA, 3, 3, 1, NA, 3,…
$ marhisp        <dbl> NA, 4, NA, 3, NA, NA, NA, NA, 3, 3, 1, NA, 3,…
$ marwht         <dbl> NA, 2, NA, 3, NA, NA, NA, NA, 3, 3, 1, NA, 3,…
$ racwork        <dbl> NA, NA, 3, 3, NA, 3, 3, 2, 1, NA, NA, 2, NA, …
$ discaff        <dbl> 2, 2, 2, 3, 3, 3, 2, 3, NA, 3, 2, 3, 3, 3, 1,…
$ yousup         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, NA, NA…
$ spwksup        <dbl> NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA…
$ fejobaff       <dbl> NA, 3, NA, 4, NA, NA, NA, NA, NA, NA, NA, NA,…
$ discaffm       <dbl> NA, 2, NA, 3, NA, NA, NA, NA, NA, NA, NA, NA,…
$ discaffw       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 2, 1, NA, NA,…
$ fehire         <dbl> NA, 3, NA, 1, NA, NA, NA, NA, 3, 1, 1, NA, 2,…
$ relpersn       <dbl> 4, 3, 3, 3, 2, 4, 3, 4, 4, 2, 3, 4, 3, 4, 3, …
$ sprtprsn       <dbl> 4, 2, 2, 3, 3, 3, 2, 3, 1, 1, 1, 3, 2, 4, 1, …
$ othlang        <dbl> 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, …
$ spklang        <dbl> NA, 3, NA, NA, NA, NA, 2, 3, NA, NA, NA, 3, N…
$ betrlang       <dbl> NA, 1, NA, NA, NA, NA, 1, 1, NA, NA, NA, 1, N…
$ letinhsp       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ letinasn       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ compuse        <dbl> NA, 1, NA, 1, NA, NA, NA, NA, 2, 2, 1, NA, 1,…
$ webmob         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, NA, NA,…
$ emailmin       <dbl> NA, 0, NA, 30, NA, NA, NA, NA, 0, 0, 15, NA, …
$ emailhr        <dbl> NA, 7, NA, 5, NA, NA, NA, NA, 1, 0, 0, NA, 6,…
$ usewww         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA…
$ wwwhr          <dbl> NA, 1, NA, 5, NA, NA, NA, NA, 14, 0, 1, NA, 3…
$ wwwmin         <dbl> NA, 0, NA, 0, NA, NA, NA, NA, 0, 0, 0, NA, 0,…
$ huclean        <dbl> 2, NA, NA, NA, NA, NA, 6, NA, 2, 1, NA, 6, 6,…
$ wrktype        <dbl> 5, NA, 5, 5, NA, 5, 5, 5, 5, NA, NA, 5, 5, 5,…
$ yearsjob       <dbl> 18.00, NA, 6.00, 5.00, NA, 6.00, 0.75, 10.00,…
$ waypaid        <dbl> 1, NA, 2, 2, NA, 2, 2, 1, 2, NA, NA, 2, 2, 2,…
$ wrksched       <dbl> 1, NA, 2, 1, NA, 3, 2, 5, 3, NA, NA, 1, 6, 1,…
$ moredays       <dbl> 5, NA, 5, 0, NA, 8, 2, 7, 5, NA, NA, 2, 2, 0,…
$ mustwork       <dbl> 2, NA, 2, 1, NA, 1, 1, 2, 2, NA, NA, 2, 2, 2,…
$ wrkhome        <dbl> 6, NA, 1, 1, NA, 1, 1, 5, 4, NA, NA, 1, 1, 1,…
$ whywkhme       <dbl> 1, NA, NA, NA, NA, NA, NA, 1, 1, NA, NA, NA, …
$ famwkoff       <dbl> 1, NA, 3, 4, NA, 3, 3, 2, 3, NA, NA, 2, 2, 1,…
$ wkvsfam        <dbl> 2, NA, 3, 1, NA, 1, 1, 3, 2, NA, NA, 4, 3, 4,…
$ famvswk        <dbl> 3, NA, 3, 4, NA, 2, 3, 3, 1, NA, NA, 3, 3, 4,…
$ hrsrelax       <dbl> 4, NA, 5, 10, NA, 4, 4, 4, 2, NA, NA, 5, 6, 1…
$ secondwk       <dbl> 2, NA, 2, 2, NA, 2, 2, 2, 1, NA, NA, 2, 2, 2,…
$ learnnew       <dbl> 2, NA, 2, 4, NA, 1, 2, 1, 2, NA, NA, 1, 2, 2,…
$ workfast       <dbl> 3, NA, 3, 1, NA, 1, 2, 3, 1, NA, NA, 2, 2, 2,…
$ workdiff       <dbl> 1, NA, 1, 4, NA, 1, 3, 1, 1, NA, NA, 1, 2, 2,…
$ overwork       <dbl> 2, NA, 3, 2, NA, 1, 3, 2, 3, NA, NA, 3, 3, 2,…
$ knowwhat       <dbl> 1, NA, 1, 1, NA, 1, 2, 2, 2, NA, NA, 2, 2, 2,…
$ myskills       <dbl> 2, NA, 2, 4, NA, 1, 3, 1, 1, NA, NA, 1, 1, 2,…
$ respect        <dbl> 1, NA, 1, 2, NA, 1, NA, 1, 2, NA, NA, 1, 2, 3…
$ trustman       <dbl> 1, NA, 2, 2, NA, 1, 3, 2, 1, NA, NA, 1, 1, 3,…
$ safetywk       <dbl> 1, NA, 2, 3, NA, 1, 4, 2, 1, NA, NA, 1, 2, 2,…
$ safefrst       <dbl> 1, NA, 2, 1, NA, 1, 3, 2, 1, NA, NA, 2, 1, 2,…
$ teamsafe       <dbl> 1, NA, 2, 2, NA, 1, 3, 2, 1, NA, NA, 1, 1, 2,…
$ safehlth       <dbl> 1, NA, 2, 1, NA, 1, 2, 1, 1, NA, NA, 2, 1, 2,…
$ proudemp       <dbl> 2, NA, 2, 2, NA, 1, 3, 2, 1, NA, NA, 2, 2, 2,…
$ prodctiv       <dbl> 2, NA, 2, 3, NA, 1, 3, 2, 2, NA, NA, 2, 3, 2,…
$ wksmooth       <dbl> 2, NA, 2, 4, NA, 1, 3, 2, 2, NA, NA, 2, 2, 2,…
$ trdunion       <dbl> 2, NA, 2, 2, NA, 1, NA, 1, NA, NA, NA, 2, 3, …
$ partteam       <dbl> 1, NA, 1, 1, NA, 1, 1, 1, 1, NA, NA, 1, 1, 1,…
$ wkdecide       <dbl> 3, NA, 2, 4, NA, 1, 1, 2, 2, NA, NA, 1, 2, 4,…
$ toofewwk       <dbl> 2, NA, 2, 1, NA, 1, 1, 1, 1, NA, NA, 3, 2, 4,…
$ promteok       <dbl> 3, NA, 3, 2, NA, 1, 3, 2, 3, NA, NA, 3, 2, 4,…
$ opdevel        <dbl> 2, NA, 3, 4, NA, 1, 3, 1, 2, NA, NA, 1, 2, 4,…
$ hlpequip       <dbl> 1, NA, 1, 3, NA, 1, 2, 1, 1, NA, NA, 1, 2, 2,…
$ haveinfo       <dbl> 1, NA, 1, 1, NA, 1, 2, 1, 1, NA, NA, 1, 2, 2,…
$ wkfreedm       <dbl> 1, NA, 2, 4, NA, 1, 2, 1, 1, NA, NA, 2, 3, 2,…
$ fringeok       <dbl> 1, NA, 3, 3, NA, 1, 2, 1, 4, NA, NA, 2, 2, 2,…
$ supcares       <dbl> 1, NA, 1, 4, NA, 1, 1, 2, 1, NA, NA, 1, 1, 1,…
$ condemnd       <dbl> 3, NA, 2, 2, NA, 1, 2, 2, 2, NA, NA, 2, 3, 1,…
$ promtefr       <dbl> 1, NA, 2, 3, NA, 2, 3, 2, 1, NA, NA, 1, 2, 2,…
$ cowrkint       <dbl> 2, NA, 2, 1, NA, 1, 2, 2, 1, NA, NA, 1, 1, 2,…
$ jobsecok       <dbl> 1, NA, 1, 2, NA, 1, 3, 1, 1, NA, NA, 1, 2, 2,…
$ suphelp        <dbl> 1, NA, 1, 4, NA, 1, 2, 2, 1, NA, NA, 1, 1, 2,…
$ wrktime        <dbl> 3, NA, 2, 3, NA, 1, 2, 2, 2, NA, NA, 1, 3, 1,…
$ cowrkhlp       <dbl> 1, NA, 1, 1, NA, 1, 1, 2, 2, NA, NA, 2, 2, 1,…
$ manvsemp       <dbl> 1, NA, 2, 3, NA, 3, 3, 3, 1, NA, NA, 2, 1, 3,…
$ hvylift        <dbl> 2, NA, 1, 1, NA, 1, 1, 2, 1, NA, NA, 1, 1, 2,…
$ handmove       <dbl> 1, NA, 2, 1, NA, 1, 1, 2, 2, NA, NA, 1, 2, 2,…
$ wkpraise       <dbl> 1, NA, 1, 1, NA, 2, 3, 2, 2, NA, NA, 1, 1, 2,…
$ fairearn       <dbl> 3, NA, 3, 3, NA, 3, 1, 2, 3, NA, NA, 2, 2, 3,…
$ rincblls       <dbl> 1, NA, 2, 2, NA, 2, 2, 1, 2, NA, NA, 1, 2, 2,…
$ laidoff        <dbl> 2, NA, 2, 2, NA, 2, 2, 2, 2, NA, NA, 2, 2, 2,…
$ jobfind1       <dbl> 3, NA, 1, 3, NA, 2, 1, 2, 2, NA, NA, 2, 2, 3,…
$ trynewjb       <dbl> 3, NA, 3, 1, NA, 2, 2, 3, 3, NA, NA, 3, 3, 3,…
$ wkageism       <dbl> 2, NA, 2, 2, NA, 2, 1, 2, 2, NA, NA, 1, 2, 2,…
$ wkracism       <dbl> 2, NA, 2, 2, NA, 2, 1, 2, 2, NA, NA, 2, 2, 2,…
$ wksexism       <dbl> 2, NA, 2, 2, NA, 2, 2, 2, 2, NA, NA, 2, 2, 2,…
$ wkharsex       <dbl> 2, NA, 2, 2, NA, 2, 2, 2, 2, NA, NA, 2, 2, 2,…
$ wkharoth       <dbl> 2, NA, 2, 2, NA, 2, 1, 2, 1, NA, NA, 2, 1, 2,…
$ health1        <dbl> 3, NA, 2, 2, NA, 3, 3, 2, 2, NA, NA, 4, 4, 3,…
$ physhlth       <dbl> 30, NA, 0, 4, NA, 0, 2, 0, 5, NA, NA, 5, 15, …
$ mntlhlth       <dbl> 15, NA, 0, 10, NA, NA, 14, 0, 5, NA, NA, 7, 1…
$ hlthdays       <dbl> 0, NA, 0, 0, NA, 0, 1, 0, 0, NA, NA, 2, 5, 5,…
$ usedup         <dbl> 4, NA, 4, 2, NA, 3, 2, 3, 3, NA, NA, 4, 2, 3,…
$ backpain       <dbl> 2, NA, 1, 2, NA, 2, 2, 2, 2, NA, NA, 2, 1, 2,…
$ painarms       <dbl> 1, NA, 2, 2, NA, 2, 1, 2, 2, NA, NA, 2, 1, 2,…
$ hurtatwk       <dbl> 0, NA, 0, 0, NA, 0, 1, 0, 0, NA, NA, 0, 0, 0,…
$ spvtrfair      <dbl> 1, NA, 1, 2, NA, 3, 2, 1, 1, NA, NA, 1, 1, NA…
$ strredpg       <dbl> 2, NA, 2, 2, NA, 2, 2, 1, 2, NA, NA, 2, 1, 1,…
$ phyeffrt       <dbl> 4, NA, 3, 2, NA, 3, 2, 4, 3, NA, NA, 4, 3, 4,…
$ slpprblm       <dbl> 1, NA, 3, 2, NA, 2, 1, 3, 3, NA, NA, 3, 2, 4,…
$ satjob1        <dbl> 2, NA, 2, 3, NA, 3, 3, NA, 1, NA, NA, 1, 1, 3…
$ knowschd       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ usetech        <dbl> 100, NA, 5, 0, NA, 75, NA, 80, 10, NA, NA, 70…
$ stress12       <dbl> NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA…
$ hyperten       <dbl> 2, NA, 2, 2, NA, 2, 2, 2, 2, NA, NA, 2, 2, 2,…
$ arthrtis       <dbl> 1, NA, 2, 2, NA, 2, 2, 2, 2, NA, NA, 2, 2, 2,…
$ diabetes       <dbl> 2, NA, 2, 2, NA, 2, 2, 2, 2, NA, NA, 2, 2, 2,…
$ depress        <dbl> 1, NA, 1, 2, NA, 2, 1, 2, 2, NA, NA, 2, 1, 2,…
$ weight         <dbl> 172, NA, 128, 155, NA, 200, 175, 152, 125, NA…
$ height         <dbl> 64, NA, 63, 63, NA, 72, 63, 68, 64, NA, NA, 6…
$ ntwkhard       <dbl> 5, NA, 0, 2, NA, 0, 25, 15, 0, NA, NA, 0, 15,…
$ misswork       <dbl> 0, NA, 0, 2, NA, 0, 2, 0, 0, NA, NA, 0, 0, 0,…
$ lifenow        <dbl> 7, NA, 5, 8, NA, 9, 7, 8, 10, NA, NA, 8, 8, 5…
$ lifein5        <dbl> 7, NA, 8, 6, NA, NA, 9, 8, NA, NA, NA, NA, 9,…
$ disrspct       <dbl> 5, 4, 4, 4, 6, 5, 2, 4, 6, NA, NA, 3, NA, 1, …
$ poorserv       <dbl> 6, 5, 4, 5, 6, 5, 2, 5, 6, NA, NA, 6, NA, 3, …
$ notsmart       <dbl> 5, 5, 4, 5, 6, 6, 3, 6, 6, NA, NA, 3, NA, 3, …
$ afraidof       <dbl> 6, 4, 5, 6, 6, 6, 3, 5, 6, NA, NA, 4, NA, 6, …
$ threaten       <dbl> 5, 5, 5, 2, 6, 6, 6, 6, 6, NA, NA, 4, NA, 6, …
$ abmoral        <dbl> 2, NA, NA, 2, 3, NA, NA, NA, NA, NA, 2, NA, N…
$ abhelp1        <dbl> 1, NA, NA, 1, 1, NA, NA, NA, NA, NA, 1, NA, N…
$ abhelp2        <dbl> 1, NA, NA, 2, 2, NA, NA, NA, NA, NA, 1, NA, N…
$ abhelp3        <dbl> 1, NA, NA, 2, 2, NA, NA, NA, NA, NA, 1, NA, N…
$ abhelp4        <dbl> 1, NA, NA, 1, 1, NA, NA, NA, NA, NA, 1, NA, N…
$ workfor1       <dbl> 1, NA, 2, NA, NA, 3, 1, 2, NA, NA, NA, 1, 1, …
$ ownstock       <dbl> 2, NA, NA, NA, NA, NA, 3, NA, NA, NA, NA, 2, …
$ stockops       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ extrapay       <dbl> 2, NA, 2, NA, NA, 2, 1, 2, NA, NA, NA, 1, 1, …
$ compperf       <dbl> NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, 1,…
$ deptperf       <dbl> NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, 2,…
$ indperf        <dbl> NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, 1,…
$ extrayr        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2…
$ numemps        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wrkslffam      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ nextgen        <dbl> 1, NA, NA, 1, 3, NA, NA, NA, NA, NA, 1, NA, N…
$ toofast        <dbl> 3, NA, NA, 1, 2, NA, NA, NA, NA, NA, 3, NA, N…
$ advfront       <dbl> 2, NA, NA, 2, 2, NA, NA, NA, NA, NA, 1, NA, N…
$ scientgo       <dbl> 1, NA, NA, 2, 2, NA, NA, NA, NA, NA, 1, NA, N…
$ scienthe       <dbl> 1, NA, NA, 1, 2, NA, NA, NA, NA, NA, 2, NA, N…
$ scientbe       <dbl> 1, NA, NA, 2, 2, NA, NA, NA, NA, NA, 2, NA, N…
$ buyvalue       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ compwage       <dbl> 3, NA, 4, NA, NA, 3, 2, 2, NA, NA, NA, 4, 3, …
$ empinput       <dbl> 2, NA, 2, NA, NA, 2, 2, 2, NA, NA, NA, 1, 2, …
$ slfmangd       <dbl> 1, NA, 2, NA, NA, 2, 2, 1, NA, NA, NA, 1, 1, …
$ emptrain       <dbl> 2, NA, 2, NA, NA, 1, 1, 2, NA, NA, NA, 1, 1, …
$ wealth         <dbl> 9, NA, 2, NA, NA, 4, 1, 8, NA, NA, NA, 2, 3, …
$ esop           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ defpensn       <dbl> 2, NA, 2, NA, NA, 1, 2, 2, NA, NA, NA, 2, 2, …
$ ratetone       <dbl> 1, NA, NA, NA, NA, NA, 2, NA, 1, 3, NA, 2, 1,…
$ posslq         <dbl> 4, 1, NA, 4, NA, 2, NA, NA, NA, NA, NA, NA, 1…
$ posslqy        <dbl> NA, NA, 4, NA, 2, NA, 4, 1, 4, 4, 4, 4, NA, N…
$ marcohab       <dbl> 3, 1, 3, 3, 2, 2, 3, 1, 3, 3, 3, 3, 1, 3, 2, …
$ healthissp     <dbl> 3, NA, 2, NA, 4, 2, 3, 2, NA, NA, NA, 4, NA, …
$ endsmeet       <dbl> NA, 3, NA, 5, NA, NA, NA, NA, 2, NA, NA, NA, …
$ goodlife       <dbl> 4, NA, 4, NA, 4, 4, 2, 4, NA, 2, 4, 3, 4, 3, …
$ famsuffr       <dbl> NA, 4, NA, 5, NA, NA, NA, NA, NA, NA, NA, NA,…
$ homekid        <dbl> NA, 4, NA, 5, NA, NA, NA, NA, 3, NA, NA, NA, …
$ housewrk       <dbl> NA, 5, NA, 3, NA, NA, NA, NA, 2, NA, NA, NA, …
$ wrkbaby        <dbl> NA, 3, NA, NA, NA, NA, NA, NA, 3, NA, NA, NA,…
$ wrksch         <dbl> NA, 2, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA,…
$ marlegit       <dbl> NA, 2, NA, 4, NA, NA, NA, NA, 4, NA, NA, NA, …
$ marmakid       <dbl> NA, 4, NA, 3, NA, NA, NA, NA, 2, NA, NA, NA, …
$ marpakid       <dbl> NA, 4, NA, 3, NA, NA, NA, NA, 2, NA, NA, NA, …
$ marhomo        <dbl> 1, 2, 1, 1, 4, 1, 2, 1, 1, NA, NA, 1, NA, 1, …
$ numkids        <dbl> NA, 2, NA, 3, NA, NA, NA, NA, NA, NA, NA, NA,…
$ kidnofre       <dbl> NA, 3, NA, 1, NA, NA, NA, NA, 4, NA, NA, NA, …
$ hubbywk1       <dbl> NA, 4, NA, 5, NA, NA, NA, NA, 4, NA, NA, NA, …
$ meovrwrk       <dbl> NA, 1, NA, 2, NA, NA, NA, NA, 3, 3, 3, NA, 4,…
$ cohabok        <dbl> NA, 2, NA, 1, NA, NA, NA, NA, 2, NA, NA, NA, …
$ laundry1       <dbl> NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ caresik1       <dbl> NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ shop1          <dbl> NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ cooking1       <dbl> NA, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ rhhwork        <dbl> NA, 20, NA, 8, NA, NA, NA, NA, 10, NA, NA, NA…
$ sphhwork       <dbl> NA, 20, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ happy7         <dbl> 4, 3, 5, 3, 4, 3, 3, 3, 2, NA, NA, 3, NA, 2, …
$ ssfchild       <dbl> NA, 4, NA, 2, NA, NA, NA, NA, 2, NA, NA, NA, …
$ ssmchild       <dbl> NA, 4, NA, 2, NA, NA, NA, NA, 2, NA, NA, NA, …
$ kidsocst       <dbl> NA, 2, NA, 3, NA, NA, NA, NA, 3, NA, NA, NA, …
$ paidlv         <dbl> NA, 0, NA, 6, NA, NA, NA, NA, 6, NA, NA, NA, …
$ paidlvdv       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA…
$ famwkbst       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, NA, NA…
$ famwklst       <dbl> NA, 3, NA, 3, NA, NA, NA, NA, 6, NA, NA, NA, …
$ careprov       <dbl> NA, 1, NA, 5, NA, NA, NA, NA, 1, NA, NA, NA, …
$ carecost       <dbl> NA, 1, NA, 3, NA, NA, NA, NA, NA, NA, NA, NA,…
$ eldhelp        <dbl> NA, 4, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA,…
$ eldcost        <dbl> NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ hhclean1       <dbl> NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ tiredhm1       <dbl> NA, 0, NA, 2, NA, NA, NA, NA, 2, NA, NA, NA, …
$ jobvsfa1       <dbl> NA, 0, NA, 2, NA, NA, NA, NA, 4, NA, NA, NA, …
$ tiredwk1       <dbl> NA, 0, NA, 3, NA, NA, NA, NA, 4, NA, NA, NA, …
$ famvswk1       <dbl> NA, 0, NA, 3, NA, NA, NA, NA, 2, NA, NA, NA, …
$ rfamlook       <dbl> NA, 2, NA, 0, NA, NA, NA, NA, 97, NA, NA, NA,…
$ spfalook       <dbl> NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ stress         <dbl> 2, NA, 4, 3, NA, 2, 2, 3, 4, NA, NA, 4, 3, 3,…
$ supervis       <dbl> 2, NA, 1, 2, NA, 2, 2, 2, 1, NA, NA, 2, 1, 2,…
$ localnum       <dbl> 2, NA, 2, 2, NA, 2, 5, 6, 1, NA, NA, 1, 2, 4,…
$ hapunhap       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ madenkid       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relactiv       <dbl> 3, 2, 1, 1, 5, 1, 1, 2, 1, 8, 1, 1, 3, 1, 3, …
$ immcrime       <dbl> 5, 3, 2, 5, 2, 5, 3, 5, 3, NA, NA, 3, NA, 3, …
$ immjobs        <dbl> 5, 2, 1, 5, 2, 5, 4, 5, 3, NA, NA, 4, NA, 3, …
$ letin1a        <dbl> 2, 3, 5, 2, 5, 2, 3, 1, NA, NA, 3, NA, 3, 2, …
$ partners       <dbl> 0, NA, 0, NA, 1, 1, 4, 1, NA, 0, 0, 1, NA, 4,…
$ matesex        <dbl> NA, NA, NA, NA, 1, 1, 1, 1, NA, NA, NA, 2, NA…
$ frndsex        <dbl> NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, 1,…
$ acqntsex       <dbl> NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, 2,…
$ pikupsex       <dbl> NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, 2,…
$ paidsex        <dbl> NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, 2,…
$ othersex       <dbl> NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA…
$ sexsex         <dbl> NA, NA, NA, NA, 3, 3, 1, 3, NA, NA, NA, 1, NA…
$ sexfreq        <dbl> 0, NA, 0, NA, 2, 2, 6, 2, NA, 0, 0, NA, NA, 4…
$ numwomen       <dbl> 0, NA, 0, NA, 4, 15, 0, 5, NA, 0, NA, 0, NA, …
$ nummen         <dbl> 25, NA, 6, NA, 0, 0, 34, 0, NA, 3, NA, 1, NA,…
$ partnrs5       <dbl> 0, NA, 1, NA, 1, 1, 7, 1, NA, 0, 0, 1, NA, 4,…
$ sexsex5        <dbl> NA, NA, 1, NA, 3, 3, 1, 3, NA, NA, NA, 1, NA,…
$ evpaidsx       <dbl> 2, NA, 2, NA, 2, 2, 2, 2, NA, 2, 2, 2, NA, 2,…
$ evstray        <dbl> 2, NA, 1, 3, 3, 3, 3, 2, 3, 3, 2, 3, NA, 3, 3…
$ condom         <dbl> 2, NA, 2, NA, 2, 1, 1, 1, NA, 2, NA, 1, NA, 1…
$ relatsex       <dbl> 2, NA, 1, NA, 1, 1, 2, 1, NA, 1, NA, 1, NA, 1…
$ evidu          <dbl> 2, NA, 2, NA, 2, 2, 2, 2, NA, 2, 2, 2, NA, 2,…
$ idu30          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ evcrack        <dbl> 2, NA, 2, NA, 2, 2, 2, 2, NA, 2, 2, 2, NA, 2,…
$ crack30        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ hivtest        <dbl> 2, NA, 2, NA, 2, 2, 1, 2, NA, 2, 2, 1, NA, 2,…
$ hivtest1       <dbl> NA, NA, NA, NA, NA, NA, 201708, NA, NA, NA, N…
$ hivtest2       <dbl> NA, NA, NA, NA, NA, NA, 5, NA, NA, NA, NA, 1,…
$ sexornt        <dbl> 3, NA, 3, NA, 3, 3, 3, 3, NA, 3, 3, 3, NA, 3,…
$ realinc        <dbl> 40900.00, NA, 18405.00, 40900.00, 11247.50, 2…
$ realrinc       <dbl> 40900.00, NA, 18405.00, 2249.50, NA, 22495.00…
$ coninc         <dbl> 63300.00, NA, 28485.00, 63300.00, 17407.50, 3…
$ conrinc        <dbl> 63300.00, NA, 28485.00, 3481.50, NA, 34815.00…
$ ethnic         <dbl> 21, 2, 8, 15, 14, 8, 17, 17, 14, 30, 11, 15, …
$ eth1           <dbl> NA, 10, 14, 26, NA, 15, NA, 14, NA, NA, NA, 2…
$ eth2           <dbl> NA, NA, 11, 11, NA, NA, NA, NA, NA, NA, NA, N…
$ eth3           <dbl> NA, NA, NA, 14, NA, NA, NA, NA, NA, NA, NA, N…
$ hispanic       <dbl> 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 30, 1, 1, 1,…
$ racecen1       <dbl> 1, 1, 1, 1, 1, 1, 16, 1, 1, NA, 1, 1, 1, 1, 1…
$ racecen2       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ racecen3       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ uscitzn        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ fucitzn        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ yearsusa       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mnthsusa       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ vetyears       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ dwelling       <dbl> 2, NA, NA, NA, NA, NA, 3, NA, 4, 6, NA, 8, 10…
$ dwelown        <dbl> NA, 1, NA, 1, NA, NA, NA, NA, 2, 2, 2, NA, 3,…
$ dwelown16      <dbl> NA, 1, NA, 1, NA, NA, NA, NA, 1, 3, 1, NA, 2,…
$ worda          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, 0, 1, NA, …
$ wordb          <dbl> NA, 1, NA, 1, NA, NA, NA, NA, 1, 1, 1, NA, 1,…
$ wordc          <dbl> NA, 1, NA, 0, NA, NA, NA, NA, 1, 0, 1, NA, 0,…
$ wordd          <dbl> NA, 1, NA, 1, NA, NA, NA, NA, 1, 1, 1, NA, 1,…
$ worde          <dbl> NA, 1, NA, 1, NA, NA, NA, NA, 1, 1, 1, NA, 1,…
$ wordf          <dbl> NA, 1, NA, 1, NA, NA, NA, NA, 1, 0, 1, NA, 1,…
$ wordg          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, 0, NA, …
$ wordh          <dbl> NA, 1, NA, 1, NA, NA, NA, NA, 0, 0, 1, NA, 1,…
$ wordi          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, NA, …
$ wordj          <dbl> NA, 1, NA, 1, NA, NA, NA, NA, 0, 0, 0, NA, 0,…
$ wordsum        <dbl> NA, 10, NA, 7, NA, NA, NA, NA, 7, 4, 8, NA, 8…
$ relate1        <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, 1…
$ gender1        <dbl> 2, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, 2…
$ old1           <dbl> 72, 80, NA, 57, NA, 27, NA, NA, NA, NA, NA, N…
$ mar1           <dbl> 3, 1, NA, 1, NA, 5, NA, NA, NA, NA, NA, NA, 1…
$ away1          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where1         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate2        <dbl> NA, 2, NA, 2, NA, 8, NA, NA, NA, NA, NA, NA, …
$ gender2        <dbl> NA, 2, NA, 2, NA, 2, NA, NA, NA, NA, NA, NA, …
$ old2           <dbl> NA, 76, NA, 56, NA, 26, NA, NA, NA, NA, NA, N…
$ mar2           <dbl> NA, 1, NA, 1, NA, 5, NA, NA, NA, NA, NA, NA, …
$ away2          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where2         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate3        <dbl> NA, NA, NA, 3, NA, NA, NA, NA, NA, NA, NA, NA…
$ gender3        <dbl> NA, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA…
$ old3           <dbl> NA, NA, NA, 23, NA, NA, NA, NA, NA, NA, NA, N…
$ mar3           <dbl> NA, NA, NA, 5, NA, NA, NA, NA, NA, NA, NA, NA…
$ away3          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where3         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate4        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gender4        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ old4           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mar4           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ away4          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where4         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate5        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gender5        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ old5           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mar5           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ away5          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where5         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate6        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gender6        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ old6           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mar6           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ away6          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where6         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gender7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ old7           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mar7           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ away7          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where7         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate8        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gender8        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ old8           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mar8           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ away8          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where8         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate9        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gender9        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ old9           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mar9           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ away9          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where9         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate10       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gender10       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ old10          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mar10          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ away10         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where10        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate11       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gender11       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ old11          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mar11          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ away11         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where11        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate12       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gender12       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ old12          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mar12          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ away12         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where12        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate13       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ gender13       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ old13          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mar13          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ away13         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where13        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relate14       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ away14         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ where14        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhhd1        <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, 1…
$ relhhd2        <dbl> NA, 2, NA, 2, NA, 3, NA, NA, NA, NA, NA, NA, …
$ relhhd3        <dbl> NA, NA, NA, 4, NA, NA, NA, NA, NA, NA, NA, NA…
$ relhhd4        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhhd5        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhhd6        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhhd7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhhd8        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhhd9        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhhd10       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhhd11       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhhd12       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhhd13       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhhd14       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ hhrace         <dbl> NA, NA, NA, NA, NA, NA, 5, NA, 1, 2, NA, 1, 1…
$ respnum        <dbl> 1, 1, NA, 3, NA, 1, NA, NA, NA, NA, NA, NA, 4…
$ hhtype         <dbl> 1, 3, NA, 41, NA, 7, NA, NA, NA, NA, NA, NA, …
$ hhtype1        <dbl> 4, 1, NA, 1, NA, 5, NA, NA, NA, NA, NA, NA, 1…
$ famgen         <dbl> 1, 1, NA, 2, NA, 1, NA, NA, NA, NA, NA, NA, 2…
$ rplace         <dbl> 1, 1, NA, 3, NA, 1, NA, NA, NA, NA, NA, NA, 4…
$ rvisitor       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ visitors       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ relhh1         <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, 1…
$ relhh2         <dbl> NA, 2, NA, 2, NA, 3, NA, NA, NA, NA, NA, NA, …
$ relhh3         <dbl> NA, NA, NA, 41, NA, NA, NA, NA, NA, NA, NA, N…
$ relhh4         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhh5         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhh6         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhh7         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhh8         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhh9         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhh10        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhh11        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhh12        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhh13        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relhh14        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp1         <dbl> NA, 2, NA, 2, NA, 3, NA, NA, NA, NA, NA, NA, …
$ relsp2         <dbl> NA, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, …
$ relsp3         <dbl> NA, NA, NA, 41, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp4         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp5         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp6         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp7         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp8         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp9         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp10        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp11        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp12        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp13        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ relsp14        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ dateintv       <dbl> 1019, 1116, 603, 718, 516, 1017, 728, 818, 80…
$ sei10          <dbl> 67.7, 73.9, 20.1, 17.0, 42.8, 20.1, 74.8, 84.…
$ sei10educ      <dbl> 76.9, 86.3, 38.4, 31.1, 37.4, 38.4, 85.7, 99.…
$ sei10inc       <dbl> 76.8, 79.7, 6.9, 5.5, 54.9, 6.9, 82.3, 72.2, …
$ pasei10        <dbl> 76.3, 26.8, 20.7, 26.8, 25.2, 62.0, 23.3, 32.…
$ pasei10educ    <dbl> 93.7, 24.3, 22.3, 24.3, 20.4, 73.0, 24.1, 25.…
$ pasei10inc     <dbl> 72.3, 26.7, 14.7, 26.7, 26.6, 68.1, 18.9, 39.…
$ masei10        <dbl> 62.9, NA, 21.6, 84.2, 38.8, 37.6, NA, 41.8, 1…
$ masei10educ    <dbl> 90.6, NA, 35.0, 97.8, 57.6, 58.9, NA, 59.2, 4…
$ masei10inc     <dbl> 43.6, NA, 9.9, 80.3, 27.8, 24.3, NA, 33.0, 5.…
$ spsei10        <dbl> NA, 59.1, NA, NA, NA, NA, NA, 84.5, NA, NA, N…
$ spsei10educ    <dbl> NA, 83.0, NA, NA, NA, NA, NA, 99.1, NA, NA, N…
$ spsei10inc     <dbl> NA, 48.2, NA, NA, NA, NA, NA, 72.2, NA, NA, N…
$ cosei10        <dbl> NA, NA, NA, NA, 24.2, 43.0, NA, NA, NA, NA, N…
$ cosei10educ    <dbl> NA, NA, NA, NA, 40.8, 55.4, NA, NA, NA, NA, N…
$ cosei10inc     <dbl> NA, NA, NA, NA, 11.3, 39.1, NA, NA, NA, NA, N…
$ copres10       <dbl> NA, NA, NA, NA, 48, 38, NA, NA, NA, NA, NA, N…
$ copres105plus  <dbl> NA, NA, NA, NA, 50, 39, NA, NA, NA, NA, NA, N…
$ uswary         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA…
$ cohort         <dbl> 1950, 1942, 1965, 1999, 1960, 1995, 2002, 197…
$ marcohrt       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ zodiac         <dbl> 1, 10, 4, 3, 11, 9, 12, 11, 11, 9, 12, 5, 4, …
$ inthisp        <dbl> 0, NA, NA, NA, NA, NA, 0, NA, 0, 0, NA, 0, 0,…
$ intrace1       <dbl> 1, NA, NA, NA, NA, NA, 1, NA, 1, 1, NA, 1, 1,…
$ intrace2       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ intrace3       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ whoelse1       <dbl> 2, NA, NA, NA, NA, NA, 2, NA, 1, 2, NA, 2, 2,…
$ whoelse2       <dbl> 2, NA, NA, NA, NA, NA, 2, NA, 2, 2, NA, 2, 2,…
$ whoelse3       <dbl> 2, NA, NA, NA, NA, NA, 2, NA, 2, 2, NA, 2, 1,…
$ whoelse4       <dbl> 2, NA, NA, NA, NA, NA, 2, NA, 2, 2, NA, 2, 2,…
$ whoelse5       <dbl> 2, NA, NA, NA, NA, NA, 2, NA, 2, 2, NA, 2, 2,…
$ whoelse6       <dbl> 2, NA, NA, NA, NA, NA, 2, NA, 2, 2, NA, 2, 2,…
$ intid          <dbl> 1, NA, NA, NA, NA, NA, 59, NA, 59, 59, NA, 59…
$ feeused        <dbl> 1, NA, NA, NA, NA, NA, 1, NA, NA, 3, NA, NA, …
$ feelevel       <dbl> 75, NA, NA, NA, NA, NA, 75, NA, NA, NA, NA, N…
$ lngthinv       <dbl> 105, 83, 156, 89, 80, 56, 88, 95, 123, 119, 3…
$ intethn        <dbl> 1, NA, NA, NA, NA, NA, 1, NA, 1, 1, NA, 1, 1,…
$ mode           <dbl> 3, 4, 4, 4, 4, 4, 1, 4, 1, 1, 4, 1, 1, 4, 3, …
$ consent        <dbl> NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, 1, NA,…
$ adminconsent   <dbl> 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, …
$ letdie1y       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, NA, …
$ ballot         <dbl> 3, 1, 3, 1, 3, 3, 3, 3, 1, 2, 2, 3, 2, 3, 1, …
$ version        <dbl> 3, 1, 3, 1, 3, 3, 3, 3, 1, 2, 2, 3, 2, 3, 1, …
$ issp           <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, …
$ formwt         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ sampcode       <dbl> 601, 601, 601, 601, 601, 601, 601, 601, 601, …
$ sample         <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 1…
$ oversamp       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ phase          <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ spanself       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ spanint        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ spaneng        <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ hlthstrt       <dbl> NA, NA, NA, NA, NA, NA, 1, NA, 1, 2, NA, 1, N…
$ huadd          <dbl> NA, NA, NA, NA, NA, 1, NA, NA, NA, 1, NA, 1, …
$ huaddwhy       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ dwellpre       <dbl> NA, NA, NA, NA, NA, 4, NA, NA, NA, 6, NA, 7, …
$ kidsinhh       <dbl> NA, NA, NA, NA, NA, 2, NA, NA, NA, 2, NA, 2, …
$ respond        <dbl> NA, NA, NA, NA, NA, 1, NA, NA, NA, 1, NA, 1, …
$ incuspop       <dbl> NA, NA, NA, NA, NA, 2, NA, NA, NA, 3, NA, 3, …
$ neisafe        <dbl> NA, NA, NA, NA, NA, 2, NA, NA, NA, 2, NA, 2, …
$ rlooks         <dbl> 3, NA, NA, NA, NA, NA, 5, NA, 5, 5, NA, 4, 4,…
$ rgroomed       <dbl> 3, NA, NA, NA, NA, NA, 3, NA, 3, 4, NA, 3, 3,…
$ rhlthend       <dbl> 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ vstrat         <dbl> 2661, 2661, 2661, 2661, 2661, 2661, 2661, 266…
$ vpsu           <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, …
$ kish           <dbl> 123126, 112214, 121324, 121324, 111231, 12114…
$ famdif16y      <dbl> NA, NA, 2, NA, NA, NA, NA, NA, 2, 1, NA, NA, …
$ pawrkslf2      <dbl> 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, NA, 3, 1, 1, NA…
$ mawrkslf2      <dbl> 1, NA, 1, 1, 1, 1, NA, 1, 2, NA, 1, 3, 1, 1, …
$ ethworld1      <dbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, …
$ ethworld2      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ ethworld3      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethworld4      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethworld5      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethworld6      <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethworld7      <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, …
$ ethworld8      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethworld9      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion1     <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion2     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion3     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion4     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion5     <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion6     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion7     <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
$ ethregion8     <dbl> 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
$ ethregion9     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion10    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion11    <dbl> 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, …
$ ethregion12    <dbl> 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, …
$ ethregion13    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion14    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion15    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion16    <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
$ ethregion17    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion18    <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion19    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion20    <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion21    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
$ ethregion22    <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion23    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion24    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion25    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion26    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion27    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion28    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion29    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion30    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion31    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion32    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion33    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion34    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion35    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion36    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion37    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion38    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion39    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion40    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion41    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion42    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion43    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion44    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion45    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion46    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion47    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion48    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion49    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion50    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion51    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion52    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion53    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion54    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion55    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion56    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion57    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion58    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion59    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion60    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion61    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion62    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion63    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion64    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion65    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion66    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion67    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion68    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion69    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion70    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion71    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion72    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion73    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion74    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion75    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion76    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion77    <dbl> 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion78    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion79    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion80    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion81    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ ethregion82    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion83    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion84    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion85    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion86    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion87    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion88    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion89    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion90    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion91    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion92    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion93    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion94    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ethregion96    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ wrkgovt1       <dbl> 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, …
$ wrkgovt2       <dbl> 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, …
$ spkathy        <dbl> NA, NA, 1, NA, 1, NA, 1, 1, 1, NA, NA, 1, NA,…
$ libathy        <dbl> NA, NA, 2, NA, 2, NA, 2, 2, 2, NA, NA, 2, NA,…
$ spkracy        <dbl> NA, NA, 1, NA, 1, NA, 2, 2, 1, NA, NA, 2, NA,…
$ libracy        <dbl> NA, NA, 2, NA, 2, NA, 1, 1, 2, NA, NA, 1, NA,…
$ spkcomy        <dbl> NA, NA, 1, NA, 1, NA, 1, 1, 2, NA, NA, 1, NA,…
$ colcomy        <dbl> NA, NA, 1, NA, 1, NA, 2, 2, 1, NA, NA, 2, NA,…
$ libcomy        <dbl> NA, NA, 2, NA, 2, NA, 1, 2, 2, NA, NA, 2, NA,…
$ spkmslmy       <dbl> NA, NA, 1, NA, 1, NA, 1, 1, 2, NA, NA, NA, NA…
$ libmslmy       <dbl> NA, NA, 2, NA, 2, NA, 2, 2, 1, NA, NA, 2, NA,…
$ polhitoky      <dbl> NA, NA, 1, NA, 2, NA, 1, 1, NA, 1, 1, 1, NA, …
$ polabusey      <dbl> NA, NA, 2, NA, 2, NA, 2, 2, NA, 2, 2, 1, NA, …
$ polattaky      <dbl> NA, NA, 1, NA, 1, NA, 1, 1, NA, 1, 1, 1, NA, …
$ raceacs1       <dbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
$ raceacs2       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ raceacs3       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ raceacs4       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs5       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs6       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs7       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs8       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs9       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs10      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs11      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs12      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs13      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs14      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs15      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ raceacs16      <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
$ abdefectg      <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ abnomoreg      <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ abhlthg        <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ abpoorg        <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ abrapeg        <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ absingleg      <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ suicide1g      <dbl> NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA,…
$ suicide2g      <dbl> NA, 1, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA,…
$ suicide3g      <dbl> NA, 1, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA,…
$ suicide4g      <dbl> NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA,…
$ maborn         <dbl> 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, …
$ paborn         <dbl> 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, NA,…
$ sexbirth1      <dbl> 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, …
$ sexnow1        <dbl> 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, …
$ hivafraid      <dbl> 1, NA, 1, NA, 2, 1, 2, 2, NA, 2, 1, 2, NA, 3,…
$ hivimmrl       <dbl> 1, NA, 1, NA, 3, 1, 1, 1, NA, 2, 1, 1, NA, 3,…
$ hivdscrm       <dbl> 4, NA, 2, NA, 2, 4, 1, 3, NA, 3, 4, 3, NA, 3,…
$ ptnrornt       <dbl> 3, NA, 3, NA, 3, 3, 3, 3, NA, 3, NA, 3, NA, 3…
$ ptnrsxbrth     <dbl> 1, NA, 1, NA, 2, 2, 1, 2, NA, 1, NA, 1, NA, 1…
$ ptnrsxnow      <dbl> 1, NA, 1, NA, 2, 2, 1, 2, NA, 1, NA, 1, NA, 1…
$ conpharvacy    <dbl> 1, NA, NA, 1, 3, NA, NA, NA, NA, NA, 2, NA, N…
$ confedvacy     <dbl> 1, NA, NA, 2, 3, NA, NA, NA, NA, NA, 1, NA, N…
$ wtssps         <dbl> 0.2310947, 0.5769942, 1.0141485, 0.9018125, 1…
$ wtssnrps       <dbl> 0.3009903, 0.7419649, 1.3638342, 1.2022913, 1…
$ uswaryv        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ prayerv        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA…
$ courtsv        <dbl> NA, NA, 3, NA, 3, NA, NA, 1, NA, NA, 3, NA, N…
$ discaffwv      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA…
$ racopenv       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ getaheadv      <dbl> NA, NA, 1, NA, 1, NA, NA, 3, NA, NA, NA, NA, …
$ divlawv        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, NA…
$ helpfulv       <dbl> NA, NA, 1, NA, 2, NA, NA, 3, NA, NA, 3, NA, N…
$ fairv          <dbl> NA, NA, 3, NA, 1, NA, NA, 3, NA, NA, 2, NA, N…
$ trustv         <dbl> NA, NA, 2, NA, 2, NA, NA, 3, NA, NA, 2, NA, N…
$ agedv          <dbl> NA, NA, 1, NA, 2, NA, NA, 3, NA, NA, 1, NA, N…
$ grassv         <dbl> NA, NA, 1, NA, NA, NA, NA, 1, NA, NA, 1, NA, …
$ relitenv       <dbl> NA, NA, 2, NA, 2, NA, NA, 4, NA, NA, 4, NA, N…
$ biblev         <dbl> NA, NA, 2, NA, 2, NA, NA, 3, NA, NA, 3, NA, N…
$ postlifev      <dbl> NA, NA, 1, NA, 1, NA, NA, 2, NA, NA, 1, NA, N…
$ kidssolv       <dbl> NA, NA, 4, NA, 3, NA, NA, 6, NA, NA, 5, NA, N…
$ uscitznv       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ fucitznv       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ fepolv         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA…
$ uswarynv       <dbl> NA, 1, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA,…
$ prayernv       <dbl> NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA,…
$ courtsnv       <dbl> 1, 2, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ discaffwnv     <dbl> NA, 2, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA,…
$ racopennv      <dbl> 2, 2, NA, 2, NA, 2, NA, NA, NA, NA, NA, NA, N…
$ getaheadnv     <dbl> 1, 1, NA, 3, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ divlawnv       <dbl> NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA,…
$ helpfulnv      <dbl> 1, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA,…
$ fairnv         <dbl> 1, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, NA,…
$ trustnv        <dbl> 2, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA,…
$ agednv         <dbl> 2, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA,…
$ grassnv        <dbl> 1, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA,…
$ relitennv      <dbl> 4, 2, NA, 4, NA, 4, NA, NA, NA, NA, NA, NA, N…
$ biblenv        <dbl> 3, 3, NA, 3, NA, 3, NA, NA, NA, NA, NA, NA, N…
$ postlifenv     <dbl> 2, 2, NA, 1, NA, 2, NA, NA, NA, NA, NA, NA, N…
$ kidssolnv      <dbl> 4, NA, NA, NA, NA, 5, NA, NA, NA, NA, NA, NA,…
$ uscitznnv      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ fucitznnv      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ fepolnv        <dbl> NA, 2, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA,…
$ abanyg         <dbl> 1, 1, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, N…
$ fileversion    <dbl> 7222.4, 7222.4, 7222.4, 7222.4, 7222.4, 7222.…
$ childsinhh     <dbl> 0, 0, NA, 0, NA, 0, NA, NA, NA, NA, NA, NA, 2…
$ adultsinhh     <dbl> 1, 2, NA, 3, NA, 2, NA, NA, NA, NA, NA, NA, 4…
$ racerank1      <dbl> 1, 1, 1, 1, 1, 1, 16, 1, 1, NA, 1, 1, 1, 1, 1…
$ racerank2      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ racerank3      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ racopeny       <dbl> NA, NA, 1, NA, 2, NA, 2, 2, 2, NA, NA, 2, NA,…
$ adoptus        <dbl> 3, 1, 3, 3, 1, 3, 3, 3, 3, NA, NA, 3, NA, 3, …
$ immfate        <dbl> 1, 1, 3, 1, 3, 1, 2, 1, NA, NA, NA, 1, NA, 1,…
$ vote20         <dbl> 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, …
$ pres20         <dbl> 1, 1, 2, 1, 2, 1, NA, 1, 1, 1, 1, 1, 3, 2, 2,…
$ if20who        <dbl> NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA…
$ wordk          <dbl> NA, 1, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA,…
$ wordl          <dbl> NA, 1, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA,…
$ wordn          <dbl> NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA,…
$ fechld2        <dbl> NA, 3, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA,…
$ fepresch2      <dbl> NA, 2, NA, 4, NA, NA, NA, NA, NA, NA, NA, NA,…
$ rspgndr        <dbl> NA, 3, NA, 5, NA, NA, NA, NA, 3, NA, NA, NA, …
$ prntlk         <dbl> NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ prntfnce       <dbl> NA, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ prntcre        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 3, NA, NA, NA…
$ prntply        <dbl> NA, 3, NA, 3, NA, NA, NA, NA, 3, NA, NA, NA, …
$ prntbhav       <dbl> NA, 3, NA, 3, NA, NA, NA, NA, 3, NA, NA, NA, …
$ prntadvs       <dbl> NA, 3, NA, 3, NA, NA, NA, NA, 3, NA, NA, NA, …
$ prntmdl        <dbl> NA, 3, NA, 3, NA, NA, NA, NA, 3, NA, NA, NA, …
$ orginc         <dbl> NA, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ plan1          <dbl> NA, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ sharehhw       <dbl> NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ clsrltv        <dbl> NA, 2, NA, 2, NA, NA, NA, NA, 3, NA, NA, NA, …
$ rsprltv1       <dbl> NA, 6, NA, 5, NA, NA, NA, NA, 4, NA, NA, NA, …
$ rsprltv2       <dbl> NA, 6, NA, 4, NA, NA, NA, NA, 2, NA, NA, NA, …
$ eldfnce        <dbl> NA, 5, NA, 4, NA, NA, NA, NA, 4, NA, NA, NA, …
$ rlyrltv        <dbl> NA, 9, NA, 1, NA, NA, NA, NA, 6, NA, NA, NA, …
$ frndfam        <dbl> NA, 3, NA, 4, NA, NA, NA, NA, 4, NA, NA, NA, …
$ cabgndr        <dbl> NA, 3, NA, 2, NA, NA, NA, NA, 3, NA, NA, NA, …
$ univgndr       <dbl> NA, 3, NA, 3, NA, NA, NA, NA, 3, NA, NA, NA, …
$ execgndr       <dbl> NA, 4, NA, 3, NA, NA, NA, NA, 3, NA, NA, NA, …
$ yrfnce         <dbl> NA, 4, NA, 3, NA, NA, NA, NA, 4, NA, NA, NA, …
$ nmbrkids       <dbl> NA, 2, NA, 1, NA, NA, NA, NA, 2, NA, NA, NA, …
$ conhlth        <dbl> 3, NA, 4, NA, 4, 5, 3, 3, NA, NA, NA, 3, NA, …
$ hlthbtr        <dbl> 5, NA, 3, NA, 5, 5, 4, 5, NA, NA, NA, 5, NA, …
$ hlthmore       <dbl> NA, NA, 4, NA, 2, 5, 3, 4, NA, NA, NA, 4, NA,…
$ hlthgov        <dbl> 5, NA, 2, NA, 5, 5, 4, 5, NA, NA, NA, 4, NA, …
$ hlthinf        <dbl> 2, NA, 4, NA, 2, 1, 3, 1, NA, NA, NA, 2, NA, …
$ hlthtax        <dbl> 2, NA, 4, NA, 5, 2, 2, 1, NA, NA, NA, 2, NA, …
$ hlthctzn       <dbl> 3, NA, 2, NA, 5, 1, 2, 1, NA, NA, NA, 2, NA, …
$ hlthdmg        <dbl> 2, NA, 2, NA, 5, 1, 2, 1, NA, NA, NA, 2, NA, …
$ hlthacc1       <dbl> 1, NA, 1, NA, 1, 1, 1, 1, NA, NA, NA, 1, NA, …
$ hlthacc2       <dbl> 5, NA, 3, NA, 4, 3, 5, 3, NA, NA, NA, 3, NA, …
$ hlthacc3       <dbl> 4, NA, 3, NA, 3, 4, 5, 4, NA, NA, NA, 4, NA, …
$ hlthacc4       <dbl> 1, NA, 3, NA, NA, 5, 2, 1, NA, NA, NA, NA, NA…
$ hlthbeh        <dbl> 3, NA, 3, NA, 3, 3, 2, 5, NA, NA, NA, 4, NA, …
$ hlthenv        <dbl> 3, NA, 3, NA, 3, 3, 2, 1, NA, NA, NA, 2, NA, …
$ hlthgene       <dbl> 3, NA, 2, NA, 3, 3, 2, 3, NA, NA, NA, 2, NA, …
$ hlthpoor       <dbl> 3, NA, 3, NA, 3, 1, 3, 1, NA, NA, NA, 2, NA, …
$ altmed         <dbl> 4, NA, 3, NA, 3, 3, 2, 3, NA, NA, NA, 3, NA, …
$ doctrst        <dbl> 2, NA, 2, NA, 4, 3, 3, 2, NA, NA, NA, 3, NA, …
$ docskls        <dbl> 3, NA, 4, NA, 2, 3, 4, 2, NA, NA, NA, 2, NA, …
$ docearn        <dbl> 3, NA, 3, NA, 2, 3, 3, 4, NA, NA, NA, 3, NA, …
$ hlthweb        <dbl> 4, NA, 5, NA, 4, 1, 5, 4, NA, NA, NA, 4, NA, …
$ hlthwblif      <dbl> 4, NA, 2, NA, 2, 5, 5, 3, NA, NA, NA, 3, NA, …
$ hlthwbanx      <dbl> 4, NA, 1, NA, 1, 5, 4, 3, NA, NA, NA, 3, NA, …
$ hlthwbvax      <dbl> 3, NA, 2, NA, 1, 5, 4, 2, NA, NA, NA, 3, NA, …
$ webhltbeh      <dbl> 3, NA, 4, NA, 3, 5, 3, 2, NA, NA, NA, 4, NA, …
$ webdocexp      <dbl> 3, NA, 4, NA, 3, 3, 2, 2, NA, NA, NA, 2, NA, …
$ websympt       <dbl> 2, NA, 4, NA, 3, 3, 4, 2, NA, NA, NA, 2, NA, …
$ webdradv       <dbl> 2, NA, 4, NA, 3, 3, 4, 2, NA, NA, NA, 2, NA, …
$ webrely        <dbl> 2, NA, 1, NA, 2, 3, 3, 4, NA, NA, NA, 4, NA, …
$ vaxdoharm      <dbl> 5, NA, 4, NA, 2, 3, 3, 5, NA, NA, NA, 4, NA, …
$ immunbetr      <dbl> 5, NA, 2, NA, 3, 3, 3, 5, NA, NA, NA, 4, NA, …
$ hlthprb        <dbl> 3, NA, 1, NA, 4, 3, 3, 2, NA, NA, NA, 2, NA, …
$ hlthpain       <dbl> 4, NA, 2, NA, 5, 3, 1, 3, NA, NA, NA, 3, NA, …
$ hlthdep        <dbl> 4, NA, 2, NA, 1, 3, 3, 2, NA, NA, NA, 2, NA, …
$ hlthconf       <dbl> 3, NA, 1, NA, 2, 3, 2, 2, NA, NA, NA, 3, NA, …
$ hlthnot        <dbl> 3, NA, 2, NA, 1, 3, 2, 2, NA, NA, NA, 1, NA, …
$ docvst         <dbl> 3, NA, 2, NA, 4, 3, 1, 2, NA, NA, NA, 2, NA, …
$ docalt         <dbl> 1, NA, 1, NA, 1, 3, 1, 1, NA, NA, NA, 1, NA, …
$ medpay         <dbl> 2, NA, 1, NA, 2, 1, 2, 2, NA, NA, NA, 1, NA, …
$ medcommt       <dbl> 1, NA, 2, NA, 2, 1, 2, 2, NA, NA, NA, 2, NA, …
$ medwtlst       <dbl> 2, NA, 2, NA, 2, 1, 2, 1, NA, NA, NA, 2, NA, …
$ medbest        <dbl> 2, NA, 3, NA, 3, 4, 3, 2, NA, NA, NA, 2, NA, …
$ hlthsat        <dbl> 3, NA, 3, NA, 5, 4, 3, 3, NA, NA, NA, 6, NA, …
$ docsat1        <dbl> 1, NA, 1, NA, 4, 4, 2, 3, NA, NA, NA, 3, NA, …
$ altsat         <dbl> 8, NA, 8, NA, 8, 4, 8, 8, NA, NA, NA, 4, NA, …
$ smokeday       <dbl> 2, NA, 5, NA, 5, 1, 1, 1, NA, NA, NA, 1, NA, …
$ drinkday1      <dbl> 1, NA, 1, NA, 3, 1, 1, 1, NA, NA, NA, 2, NA, …
$ physact        <dbl> 2, NA, 2, NA, 3, 4, 4, 2, NA, NA, NA, 3, NA, …
$ frtvegs        <dbl> 5, NA, 3, NA, 4, 4, 5, 5, NA, NA, NA, 5, NA, …
$ disblty        <dbl> 2, NA, 2, NA, 1, 2, 2, 2, NA, NA, NA, 1, NA, …
$ weight_issp    <dbl> 172, NA, 128, NA, 175, 200, 175, 152, NA, NA,…
$ shutbus        <dbl> 1, NA, 3, NA, 1, 1, 2, 1, NA, NA, NA, 1, NA, …
$ stayhome       <dbl> 1, NA, 3, NA, 2, 1, 1, 2, NA, NA, NA, 2, NA, …
$ mobilsurv      <dbl> 1, NA, 4, NA, 3, 1, 2, 4, NA, NA, NA, 3, NA, …
$ reqmasks       <dbl> 1, NA, 3, NA, 1, 1, 1, 1, NA, NA, NA, 1, NA, …
$ bangather      <dbl> 1, NA, 3, NA, 1, 1, 1, 2, NA, NA, NA, 1, NA, …
$ stockval1      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ stockyr        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ stockyrval     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ stockoptyr     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ stoptyramt     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ extr2021       <dbl> NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, 2,…
$ extraval1      <dbl> NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA…
$ yearval1       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4…
$ numorg1        <dbl> 4, NA, NA, NA, NA, 2, 4, 5, NA, NA, NA, 1, 11…
$ perfrt         <dbl> 3, NA, 4, NA, NA, 3, 3, 4, NA, NA, NA, 3, 3, …
$ chngtime       <dbl> 1, NA, 3, 1, NA, 4, 3, 1, 2, NA, NA, 1, 2, 4,…
$ wrkmeangfl     <dbl> 3, NA, 2, 4, NA, 1, 2, 2, 1, NA, NA, 2, 2, 2,…
$ strmgtsup      <dbl> 4, NA, 2, 2, NA, 1, 3, 3, 5, NA, NA, 4, 4, 3,…
$ psysamephys    <dbl> 4, NA, 2, 1, NA, 1, 2, 3, 3, NA, NA, 4, 3, 3,…
$ allorglevel    <dbl> 2, NA, 2, 1, NA, 5, 2, 3, 4, NA, NA, 4, 3, 3,…
$ feelnerv       <dbl> 2, NA, 1, 2, NA, 3, 3, 2, 1, NA, NA, 2, 3, 1,…
$ worry          <dbl> 2, NA, 1, 1, NA, 2, 2, 1, 1, NA, NA, 2, 2, 1,…
$ feeldown       <dbl> 2, NA, 1, 1, NA, 2, 3, 1, 1, NA, NA, 1, 1, 1,…
$ nointerest     <dbl> 2, NA, 1, 1, NA, 3, 2, 1, 1, NA, NA, 1, 1, 1,…
$ svyenjoy       <dbl> 2, 4, 2, 4, 5, 2, 2, 5, 2, 3, 2, 4, 2, 3, 1, …
$ svyid1         <dbl> 2, 3, 2, 4, 4, 3, 2, 4, 1, 2, 3, 3, 2, 1, 2, …
$ svyid2         <dbl> 1, 3, 3, 4, 4, 3, 2, 4, 2, 1, 4, 3, 2, 3, 1, …
$ baselinestatus <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ amerstatus     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ yrlvmus        <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 1, NA,…
$ yrartxbt       <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 2, NA,…
$ yrmovie        <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 1, NA,…
$ artsout        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA…
$ yrcreat        <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA…
$ yrrdg          <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 1, NA,…
$ yrtour         <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 2, NA,…
$ yrstmus        <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 1, NA,…
$ yrarmus        <dbl> NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, 1, NA,…
$ yrstpo         <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 2, NA,…
$ yrarpo         <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 2, NA,…
$ yrclass        <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 2, NA,…
$ yrpod          <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 1, NA,…
$ cvdlvmus       <dbl> NA, NA, NA, NA, 4, NA, NA, NA, NA, NA, 1, NA,…
$ cvdart         <dbl> NA, NA, NA, NA, 4, NA, NA, NA, NA, NA, 3, NA,…
$ cvdmov         <dbl> NA, NA, NA, NA, 4, NA, NA, NA, NA, NA, 1, NA,…
$ cvdcreat       <dbl> NA, NA, NA, NA, 4, NA, NA, NA, NA, NA, 4, NA,…
$ cvdrdg         <dbl> NA, NA, NA, NA, 4, NA, NA, NA, NA, NA, 1, NA,…
$ cvdtour        <dbl> NA, NA, NA, NA, 4, NA, NA, NA, NA, NA, 2, NA,…
$ cvdstmus       <dbl> NA, NA, NA, NA, 4, NA, NA, NA, NA, NA, 2, NA,…
$ cvdarmus       <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 2, NA,…
$ cvdstpo        <dbl> NA, NA, NA, NA, 4, NA, NA, NA, NA, NA, 4, NA,…
$ cvdarpo        <dbl> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, 2, NA,…
$ cvdclass       <dbl> NA, NA, NA, NA, 4, NA, NA, NA, NA, NA, 4, NA,…
$ cvdpod         <dbl> NA, NA, NA, NA, 4, NA, NA, NA, NA, NA, 2, NA,…
$ neastatus      <dbl> NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, 1, NA,…
$ wrkwayup_next  <dbl> 5, NA, NA, 5, 2, NA, NA, NA, NA, NA, 5, NA, N…
$ blkmblty       <dbl> 2, NA, NA, 1, 4, NA, NA, NA, NA, NA, 2, NA, N…
$ blkdsrv        <dbl> 1, NA, NA, 1, 3, NA, NA, NA, NA, NA, 3, NA, N…
$ blktry         <dbl> 5, NA, NA, 4, 2, NA, NA, NA, NA, NA, 5, NA, N…
$ brv5           <dbl> 1, NA, NA, 1, 0, NA, NA, NA, NA, NA, 1, NA, N…
$ brv5sp         <dbl> 0, NA, NA, 0, NA, NA, NA, NA, NA, NA, 1, NA, …
$ brv5par        <dbl> 0, NA, NA, 0, NA, NA, NA, NA, NA, NA, 0, NA, …
$ brv5grand      <dbl> 0, NA, NA, 0, NA, NA, NA, NA, NA, NA, 0, NA, …
$ brv5child      <dbl> 0, NA, NA, 0, NA, NA, NA, NA, NA, NA, 0, NA, …
$ brv5sib        <dbl> 0, NA, NA, 0, NA, NA, NA, NA, NA, NA, 1, NA, …
$ brv5oth        <dbl> 1, NA, NA, 1, NA, NA, NA, NA, NA, NA, 0, NA, …
$ brv5spnum      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA…
$ brv5partnum    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA…
$ brv5dadnum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv5momnum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv5filnum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv5milnum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv5gmanum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv5gpanum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv5sonnum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv5daunum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv5chinum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv5bronum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA…
$ brv5sisnum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA…
$ brv5silnum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA…
$ brv5cuznum     <dbl> 1, NA, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA,…
$ brv5frndnum    <dbl> 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ brv5cowknum    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv5othnum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv16          <dbl> 1, NA, NA, 1, 1, NA, NA, NA, NA, NA, 1, NA, N…
$ brv16sp        <dbl> 0, NA, NA, 0, 0, NA, NA, NA, NA, NA, 0, NA, N…
$ brv16par       <dbl> 0, NA, NA, 0, 0, NA, NA, NA, NA, NA, 0, NA, N…
$ brv16sib       <dbl> 0, NA, NA, 0, 0, NA, NA, NA, NA, NA, 0, NA, N…
$ brv16grand     <dbl> 1, NA, NA, 1, 1, NA, NA, NA, NA, NA, 0, NA, N…
$ brv16oth       <dbl> 0, NA, NA, 0, 0, NA, NA, NA, NA, NA, 1, NA, N…
$ brv16spnum     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv16partnum   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv16dadnum    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv16momnum    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv16filnum    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv16milnum    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv16gmanum    <dbl> 1, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA,…
$ brv16gpanum    <dbl> 1, NA, NA, 2, 0, NA, NA, NA, NA, NA, NA, NA, …
$ brv16bronum    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv16sisnum    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv16silnum    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brv16cuznum    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA…
$ brv16frndnum   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA…
$ brv16othnum    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA…
$ gestate        <dbl> NA, NA, NA, 1, 2, NA, NA, NA, NA, NA, 1, NA, …
$ abgender       <dbl> 2, NA, NA, 2, 2, NA, NA, NA, NA, NA, 2, NA, N…
$ abbelief       <dbl> 1, NA, NA, 1, 3, NA, NA, NA, NA, NA, 1, NA, N…
$ vaxhstncy      <dbl> 5, NA, NA, 4, 4, NA, NA, NA, NA, NA, 5, NA, N…
$ vaxkids        <dbl> 1, NA, NA, 2, 3, NA, NA, NA, NA, NA, 1, NA, N…
$ vaxsafe        <dbl> 1, NA, NA, 2, 4, NA, NA, NA, NA, NA, 2, NA, N…
$ fluvax         <dbl> 1, NA, NA, 2, 2, NA, NA, NA, NA, NA, 2, NA, N…
$ covid12        <dbl> 1, NA, NA, 1, 2, NA, NA, NA, NA, NA, 1, NA, N…
$ covemply       <dbl> 2, NA, NA, NA, 1, NA, NA, NA, NA, NA, 5, NA, …
$ pandinc        <dbl> 3, NA, NA, 2, 3, NA, NA, NA, NA, NA, 2, NA, N…
$ pandmet        <dbl> 4, NA, NA, 2, 3, NA, NA, NA, NA, NA, 1, NA, N…
$ biokids        <dbl> 1, NA, NA, 0, 2, NA, NA, NA, NA, NA, 2, NA, N…
$ malekids       <dbl> 0, NA, NA, 0, 1, NA, NA, NA, NA, NA, 0, NA, N…
$ firstkidsex    <dbl> 2, NA, NA, NA, 1, NA, NA, NA, NA, NA, 2, NA, …
$ nonbinkids     <dbl> 2, NA, NA, 2, 2, NA, NA, NA, NA, NA, 2, NA, N…
$ femself        <dbl> 5, NA, NA, 6, 1, NA, NA, NA, NA, NA, 5, NA, N…
$ mascself       <dbl> NA, NA, NA, 1, 7, NA, NA, NA, NA, NA, 1, NA, …
$ nextstatus     <dbl> 1, NA, NA, 1, 1, NA, NA, NA, NA, NA, 1, NA, N…
$ worksick       <dbl> 90, NA, 0, 3, NA, 0, 1, 0, 3, NA, NA, 5, 3, 0…
$ fund_next      <dbl> 3, NA, NA, 3, 2, NA, NA, NA, NA, NA, 3, NA, N…
$ hompop_exp     <dbl> 1, 1, 3, 1, 3, 1, 3, 2, 2, 1, 2, 1, 1, 1, 3, …
$ modesequence   <dbl> 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, …
$ rheight        <dbl> 64, NA, 63, NA, 71, 72, 63, 69, NA, NA, NA, 6…
$ instype01      <dbl> 3, NA, 5, NA, 1, 1, 1, 3, NA, NA, NA, 2, NA, …
$ instype02      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ instype03      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ instype04      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ totalincentive <dbl> 177, 200, 52, 127, 87, 102, 102, 57, 102, 2, …
$ babies_exp     <dbl> NA, NA, 0, NA, 0, NA, 0, 0, 1, 0, 0, 0, NA, N…
$ preteen_exp    <dbl> NA, NA, 0, NA, 0, NA, 0, 0, 0, 0, 0, 0, NA, N…
$ teens_exp      <dbl> NA, NA, 0, NA, 0, NA, 2, 0, 0, 0, 0, 0, NA, N…
$ adults_exp     <dbl> NA, NA, 3, NA, 3, NA, 1, 2, 1, 1, 2, 1, NA, N…
$ childs_exp     <dbl> NA, NA, 0, NA, 0, NA, 2, 0, 1, 0, 0, 0, NA, N…
$ respnumh       <dbl> 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 3, 1, 2, 1, 2, …
$ hefinfo1       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ famgen_exp     <dbl> NA, NA, 2, NA, 2, NA, 1, 1, 2, 1, 2, 1, NA, N…
$ hhtype1_exp    <dbl> NA, NA, 3, NA, 5, NA, 2, 1, 2, 4, 3, 4, NA, N…
$ batch          <dbl> NA, 2, 1, 2, 1, NA, 2, NA, 2, NA, 2, NA, NA, …
$ subsamprate    <dbl> 1.0, NA, NA, NA, NA, 1.0, NA, 1.0, NA, 1.0, N…
$ wtssps_nea     <dbl> NA, NA, NA, NA, 1.7044439, NA, NA, NA, NA, NA…
$ wtssnrps_nea   <dbl> NA, NA, NA, NA, 3.2197778, NA, NA, NA, NA, NA…
$ wtssps_next    <dbl> 0.2309795, NA, NA, 0.8807435, 1.6932353, NA, …
$ wtssnrps_next  <dbl> 0.2674994, NA, NA, 1.0893818, 2.1217118, NA, …
$ wtssps_as      <dbl> 0.2764242, 0.6735305, 1.2107570, 1.0831560, 1…
$ wtssnrps_as    <dbl> 0.3762613, 0.8941144, 1.6817922, 1.4858712, 1…

Introducing abany

The GSS abany item asks “Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if the woman wants it for any reason?” The answers are “yes” (1) and “no” (2).

There is also a web version of this in 2022: abanyng. We will drop this for now.

Warning

We do not want variables coded 1 and 2. As we will see later, it’s better if (almost) all variables have a meaningful 0 value.

Wrangling abany

d <- gss2022 |> 
  select(abany) 

d |> group_by(abany) |> 
  summarize(n = n())
# A tibble: 3 × 2
  abany     n
  <dbl> <int>
1     1   799
2     2   546
3    NA  2804
d <- d |> 
  drop_na() |>   # drop NA values
  mutate(abany = case_match(abany,
                            1 ~ 1,
                            2 ~ 0 ))

d |> pull(abany) |> table()

  0   1 
546 799

Tip

case_match() is useful for recoding variables.

abany sample proportion

We can use mean() to calculate the sample proportion.

mean(d$abany)
[1] 0.594052

We find that 59.4% of our sample supports abortion rights for any reason.

Inference

How close is this sample statistic to the population parameter? We’ll never know.

We can use a simulation to give us a sense of what kind of accuracy is possible with a sample of 1345 respondents.

Random number functions

We could build an imaginary US adult population where 59.4% of adults support abortion rights. But we can instead use random number functions to draw a sample from an infinite population instead.

set.seed(722)
rbinom(n = 1345, size = 1, prob = .594) |> # sample from inf. pop.
  mean()                                   # take the mean
[1] 0.5635688

Why 59.4%?

Why assume that the population parameter is .594? Because we are interested in how widely spread the simulations are and there isn’t a more reasonable value to choose.

Taking many samples (again)

Let’s do this 5000 times and collect the results.

set.seed(722)
gss_sims <- tibble(
  sim_id = 1:5000) |> 
  rowwise() |> 
  mutate(samp_prop = mean(rbinom(1345, 1, .594)))

The IQR tells us how spread out the middle half of the estimates are.

gss_sims |> pull(samp_prop) |> IQR()
[1] 0.01858736

Thus, with 1345 cases, half of the sample proportions will be within approximately 1.9 points of the true value.

Visualize

Summary

We still don’t know the true value, of course. We are probably within a couple of points of the true value. But we could be 3 (or possibly more) points away.

Recap

  • we want to know about populations
  • we end up having to use samples
  • samples are random subsets of the population that are expensive to collect
  • the larger the sample, the more accurately we can infer the population proportion
  • we can use simulations to understand how this works.

Homework

  • make a fake population of at least 100,000 inhabitants OR use random number functions
  • make some people do/think/believe/are X (1) and some people not-X (0)
  • write a function that samples from that population using 3 or more different sample sizes
  • plot and interpret your results
  • push it to GitHub the night before the next lecture

Message me on Slack if you are struggling!

Probability

From simulations to laws

We saw two things in the simulations:

  1. As the number of simulations gets bigger, the clearer the pattern we observe

  2. The pattern we see is a symmetrical distribution centered on the “true” value

This relates to two rules that are important in statistics.

Law of Large Numbers

The average of the results (e.g., means) obtained from a large number of independent random samples converges to the true value as the number of samples increases.

This applies to a single large sample or to the sum of many smaller samples (as we did with the simulations).

This only works because each observation (e.g., person) is randomly sampled.

Law of Large Numbers

The observed value eventually converges to .594. It’s still not perfect here!

Central limit theorem

In the long run, the distribution of averages of any distribution converges to the normal distribution.

Note

The normal distribution is that “bell-shaped” distribution you saw last time. We will define it more formally later on.

CLT demo

It doesn’t matter what the shape of the empirical distribution is. Repeated estimates of its mean will form a normal distribution.

tvdata <- gss2022 |> 
  select(tvhours) |> 
  drop_na()

ggplot(tvdata,
       aes(x = tvhours)) +
  geom_bar(fill = "gray")

CLT demo

Distribution of sampled means

# get sampling function
get_tv_mean <- function() {
  slice_sample(tvdata, 
               n = nrow(tvdata),  # sample size = data size
               replace = TRUE) |> # replacement
    pull(tvhours) |> 
    mean()
}

# draw samples
set.seed(722)
tv_samples <- tibble(
  samp_id = 1:5000) |> 
  rowwise() |> 
  mutate(samp_mean = get_tv_mean())

# plot
ggplot(tv_samples,
       aes(x = samp_mean)) +
  geom_histogram(fill = "gray",
                 color = "white",
                 binwidth = .025)

Distribution of sampled means

Summary

We will return to this. For now, it’s important to remember:

  1. The Law of Large Numbers states that repeated random observations will converge to the true value;

  2. The Central Limit Theorem states that estimates (e.g., means) from repeated random samples will form a normal distribution regardless of the data distribution.

Warning

Non-random samples, no matter how large, will not converge to the true population value!

Putting this into practice

We aren’t totally ready for this (and we’ll come back) but here’s why this matters. If we have a “large enough” sample (just one, real-life sample), we can use that to estimate the uncertainty of the sampling process.

For example, if we had a sample of 400, 70% of whom are parents, we could say that, if we repeated our experiment infinite times, 95% of the estimated sample proportions would be between .655 and .745.

The formula for this is \(\hat{p} \pm 1.96 \times \sqrt{\hat{p}(1-\hat{p}) / n}\). But don’t worry about that for now!

Probability basics

To really get this, we need probability. We haven’t defined it formally, but we’ve been using it in this course.

For example, when we defined a population of 100,000, exactly 70,000 of whom were parents, and sampled them at random, we made it so each “draw” had a probability of .7 of being a parent.

Note

Technically this isn’t true unless we do sampling with replacement. Otherwise the probability would change slightly with each draw.

Probability of an event

Let’s return to the abortion rights example. In the 2022 GSS, there are two possibilities: support abortion rights, \(S\), or oppose abortion rights, \(O\). These are complementary events, so \(O = \neg S\).

Together, these represent the event space, or the set of things that can happen in one event (sampling a person). We can write \(\Omega = \{S, \neg S\}\). These are mutually exclusive events.

Since 59.4% of sample respondents support, we can say \(P(S) = .594\) and \(P(\neg S) = .406\). \(P(x)\) or \(Pr(x)\) means “the probability of \(x\).”

Probability of two events

If \(P(S) = .594\), what is the probability of sampling two people in a row who both support abortion rights?

These events are independent so the probability is \(.594 \times .594 \approx .353\). Independent here means that the result of the each draw has no effect on the value of other draws.

Multiple attributes

Let’s look at a dataset with multiple variables per respondent. Let’s consider whether each respondent is “very happy” and whether the respondent has a college degree. To do this, we’ll need to do some wrangling.

d <- gss2022 |> 
  select(happy, educ) |> 
  drop_na() |> 
  mutate(vhappy = if_else(happy == 1, 
                          "Very happy", 
                          "Not very" ),
         college = if_else(educ >= 16, 
                           "College", 
                           "Not college")) |> 
  select(vhappy, college)

Contingency table

college Not very Very happy
College 1171 357
Not college 2055 510

This isn’t very “tidy” but we can wrangle this into a 2x2 table of counts of these variables. This is called a contingency table.

Marginal probability

college Not very Very happy
College 1171 357
Not college 2055 510

The marginal probability is the probability of an event related to one variable without regard for the the other variable.

So what is the marginal probability of having a college degree, \(P(\text{College})\)? What is the marginal probability of being very happy, \(P(\text{Very happy})\)?

Joint probability

college Not very Very happy
College 1171 357
Not college 2055 510

The joint probability is the probably of two events happening at the same time.

What is the joint probability of having a college degree and being very happy, \(P(\text{College} \cap \text{Very happy})\)?

Joint probability visualized

Product of marginal probabilities

Why isn’t the joint probability here (0.087) the same as the product of the marginal probabilities (0.079)?

What would it mean if this were true? (It’s not.)

\[P(\text{College}) \times P(\text{Very happy}) = \\ P(\text{College} \cap \text{Very happy})\]

It would mean that the two variables were independent, i.e., that knowing one tells you nothing about the other.

Conditional probability

The final type we’ll learn is conditional probability. This is the probability of a specific outcome conditional on the value of another variable.

Conditional example (1)

What is the probability of being very happy conditional on having a college degree?

college Not very Very happy
College 1171 357
Not college 2055 510

\(P(\text{VH} | \text{C}) =\) 0.234

Conditional example (2)

What is the probability of being very happy conditional on NOT having a college degree?

college Not very Very happy
College 1171 357
Not college 2055 510

\(P(\text{VH} | \neg \text{C}) =\) 0.199

If these conditional probabilities were the same, the two variables would be independent.

Conditional probability visualized

Summary

This is a very basic introduction to probability. We will build on it but it’s important to understand the fundamentals of marginal, joint, and conditional probability.

Homework

  1. Create two different two-by-two tables, at least one of which is from the GSS. Make sure to use drop_na() to exclude missing data for now.

  2. Compute and interpret all the marginal, joint, and conditional probabilities for each table.